miR-126-3p Inhibits Thyroid Cancer Cell Growth and Metastasis, and Is Associated with Aggressive Thyroid Cancer

<div><p>Background</p><p>Previous studies have shown that microRNAs are dysregulated in thyroid cancer and play important roles in the post-transcriptional regulation of target oncogenes and/or tumor suppressor genes.</p><p>Methodology/Principal Findings</p><p>We studied the function of miR-126-3p in thyroid cancer cells, and as a marker of disease aggressiveness. We found that miR-126-3p expression was significantly lower in larger tumors, in tumor samples with extrathyroidal invasion, and in higher risk group thyroid cancer in 496 papillary thyroid cancer samples from The Cancer Genome Atlas study cohort. In an independent sample set, lower miR-126-3p expression was observed in follicular thyroid cancers (which have capsular and angioinvasion) as compared to follicular adenomas. Mechanistically, ectopic overexpression of miR-126-3p significantly inhibited thyroid cancer cell proliferation, <i>in vitro</i> (p<0.01) and <i>in vivo</i> (p<0.01), colony formation (p<0.01), tumor spheroid formation (p<0.05), cellular migration (p<0.05), VEGF secretion and endothelial tube formation, and lung metastasis <i>in vivo</i>. We found 14 predicted target genes, which were significantly altered upon miR-126-3p transfection in thyroid cancer cells, and which are involved in cancer biology. Of these 14 genes, <i>SLC7A5</i> and <i>ADAM9</i> were confirmed to be inhibited by miR-126-3p overexpression and to be direct targets of miR-136-3p.</p><p>Conclusions/Significance</p><p>To our knowledge, this is the first study to demonstrate that miR-126-3p has a tumor-suppressive function in thyroid cancer cells, and is associated with aggressive disease phenotype.</p></div

MiR-126-3p overexpression inhibits the migration of thyroid cancer cells.

<p>Wound healing assay <b>(A)</b> and Boyden chamber assay <b>(B)</b> data. MiR-126-3p overexpression significantly decreased wound width closure at 24 hours in all thyroid cancer cell lines studied. The Y axis represents the wound distance. Error bars represent SEM (** indicates p<0.01; *** indicates p<0.001). MiR-126-3p overexpression significantly decreased the number of migrated cells in all thyroid cancer cells in the Boyden chamber assay. The Y axis represents the number of migrated cells per field. Error bars represent SEM (* indicates p<0.05; ** indicates p<0.01; *** indicates p<0.001).</p

miR-126-3p regulates and directly targets SLC7A5 and ADAM9 protein expression in thyroid cancer cells <i>in vitro</i> and <i>in vivo</i>.

<p><b>(A)</b> Immunoblots of SLC7A5 and GAPDH in TPC-1 and XTC-1 cell lines, which were transfected with either miR-126-3p or miR-NC for 72 hours. The FTC-133 cell line had no detectable protein expression for SLC7A5. <b>(B)</b> Immunoblots for ADAM9 and GAPDH in TPC-1, FTC-133 and XTC-1 cell lines, which were transfected with either miR-126-3p or miR-NC for 72 hours <i>in vitro</i>. <b>(C)</b> Immunoblots for detecting ADAM9 and GAPDH in FTC-133-Luc2 tumor xenografts that had been inoculated subcutaneously into the flanks of athymic nude mice and allowed to develop for 10 days. <b>(D)</b> Luciferase activity of pEZX-SLC7A5-3′UTR and pEZX-SLC7A5-3′UTR in FTC-133 cells when co-transfected with miR-126-3p or miR-NC. All luciferase measurements were made in triplicate and readings were performed 24 hours post-transfection. Error bars represent SEM (*** indicates p<0.001). <b>(E)</b> The expression level of miR-126-3p is significantly inversely associated with the expression level of <i>SLC7A5</i> in 481 papillary thyroid cancer samples from the TCGA dataset. *** indicates p<0.001.</p

MiR-126-3p overexpression inhibits tumor growth and tumor metastasis <i>in vivo</i>.

<p><b>(A)</b> Growth of tumor xenografts in nude mice. <b>Left panel</b>: representative images of mice xenograft size at autopsy. <b>Middle and right panel</b>: tumor luciferase activity and tumor volume measurement, and weight. FTC-133-Luc2 cells transfected with miR-126-3p and miR-NC were inoculated subcutaneously in the flanks of athymic nude mice. <b>(B)</b> Tumor metastasis. <b>Left panel</b>: Representative images of mice with metastases showing luminescence signal. <b>Middle panel</b>: Quantification of luminescence signal intensity differences between miR-126-3p and miR-NC. FTC-133-Luc2 cells transfected with miR-126-3p and miR-NC were injected into athymic nude mice via the tail vein, and the mice were imaged with a Xenogen IVIS 100 system. The relative luminescence signal of each mouse is calculated as the ratio of original signal to the signal taken 14 days post-injection. The images shown here were taken 7 weeks after vein injection of tumor cells. Error bars represent SEM (* indicates p<0.05; ** indicates p<0.01; *** indicates p<0.001). All animal experiments were repeated twice. <b>Right panel</b>: A representative microscopic image (hematoxylin and eosin [H&E] staining) of metastatic lung tumor induced by FTC-133-Luc2 cells transfected with miR-NC and an H&E-stained section of metastatic lung tumor induced by FTC-133-Luc2 cells transfected with miR-126-3p.</p

miR-126-3p overexpression inhibits cellular proliferation, and colony and spheroid formation.

<p><b>(A–C)</b> Thyroid cancer cell line proliferation with miR-126-3p overexpression. The Y axis represents the cell number. Error bars represent the standard error of the mean (SEM). (* indicates p<0.05; ** indicates p<0.01; *** indicates p<0.001). <b>(D)</b> miR-126-3p overexpression inhibits colony formation in thyroid cancer cells. Colony numbers in FTC-133 cell lines. The Y axis represents the number of colonies per field. Error bars represent SEM (*** indicates p<0.001). <b>(E)</b> miR-126-3p overexpression decreases the size and number of spheroids. Top panel: representative image of spheroids in culture with miR-126-3p overexpression (FTC-133 cells). Lower panel: Quantification of spheroid differences between XTC-1 and FTC-133 cells with miR-126-3p overexpression. The total area occupied by the spheroids within an image was measured by circumscribing the perimeter of each spheroid, marking the entire area, and calculating the pixel numbers with ImageJ software (National Institutes of Health, Bethesda, MD, USA). The Y axis represents the size and number of the spheroids. Error bars represent SEM (* indicates p<0.05).</p

Lower miR-126-3p expression is associated with aggressive thyroid cancer.

<p><b>(A)</b> miR-126-3p is significantly lower in larger papillary thyroid cancer (T3 compared to T2 and T1 tumors). Data for TCGA samples with available tumor size data. T1 tumors less than 2cm and not growing outside the thyroid, T2 tumors > 2cm but < 4cm and not growing outside the thyroid, T3 tumors measuring > 4cm or growing outside the thyroid. ** indicates p < 0.01, *** indicates p < 0.001. Y axis is log₁₀ normalized expression. <b>(B)</b> miR-126-3p expression is significantly lower in papillary thyroid cancer with minimal and moderate extrathyroidal invasion. ** indicates p < 0.01, *** indicates p < 0.001. Y axis is log₁₀ normalized expression. <b>(C)</b> miR-126-3p expression is significantly lower in high and intermediate MACIS risk papillary thyroid cancer as compared to low MACIS risk tumors. MACIS is a prognostic scoring system used in the TCGA database which is based on the presence of Metastasis, patient Age, Completeness of resection, local Invasion, and tumor Size. ** indicates p < 0.01, *** indicates p < 0.001. Y axis is log₁₀ normalized expression. <b>(D)</b> miR-126-3p expression is significantly lower in follicular thyroid cancer than follicular thyroid adenoma. All follicular thyroid cancer case had histologic evidence of capsular and vascular invasion and the adenomas did not have any evidence of capsular and vascular invasion. ** indicates p < 0.01. Y axis is 2^-(ΔΔCt).</p

MEK inhibitor GSK1120212-mediated radiosensitization of pancreatic cancer cells involves inhibition of DNA double-strand break repair pathways

<p><b>Purpose:</b> Over 90% of pancreatic adenocarcinoma PC express oncogenic mutant KRAS that constitutively activates the Raf-MEK-MAPK pathway conferring resistance to both radiation and chemotherapy. MEK inhibitors have shown promising anti-tumor responses in recent preclinical and clinical studies, and are currently being tested in combination with radiation in clinical trials. Here, we have evaluated the radiosensitizing potential of a novel MEK1/2 inhibitor GSK1120212 (GSK212,or trametinib) and evaluated whether MEK1/2 inhibition alters DNA repair mechanisms in multiple PC cell lines.</p> <p><b>Methods</b>: Radiosensitization and DNA double-strand break (DSB) repair were evaluated by clonogenic assays, comet assay, nuclear foci formation (γH2AX, DNA-PK, 53BP1, BRCA1, and RAD51), and by functional GFP-reporter assays for homologous recombination (HR) and non-homologous end-joining (NHEJ). Expression and activation of DNA repair proteins were measured by immunoblotting.</p> <p><b>Results:</b> GSK212 blocked ERK1/2 activity and radiosensitized multiple KRAS mutant PC cell lines. Prolonged pre-treatment with GSK212 for 24-48 hours was required to observe significant radiosensitization. GSK212 treatment resulted in delayed resolution of DNA damage by comet assays and persistent γH2AX nuclear foci. GSK212 treatment also resulted in altered BRCA1, RAD51, DNA-PK, and 53BP1 nuclear foci appearance and resolution after radiation. Using functional reporters, GSK212 caused repression of both HR and NHEJ repair activity. Moreover, GSK212 suppressed the expression and activation of a number of DSB repair pathway intermediates including BRCA1, DNA-PK, RAD51, RRM2, and Chk-1.</p> <p><b>Conclusion:</b> GSK212 confers radiosensitization to KRAS-driven PC cells by suppressing major DNA-DSB repair pathways. These data provide support for the combination of MEK1/2 inhibition and radiation in the treatment of PC.</p

Workflow to mine frequent co-expression network using QCM from cancer and normal tissue microarray datasets.

<p>Blue ovals indicate gene members shared by different networks, ovals in other colors indicate genes unique to each network.</p

Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability

<div><p>Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well- known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.</p> </div

Kaplan-Meier curve of breast cancer, glioblastoma (GBM) and ovarian cancer (OV) using network genes identified from cancer datasets.

<p>The p-values are computed using Log- rank test with 100 repeats. A: using Network 1 genes on NKI mixed cohort; B: using Van't Veer 70-gene signature <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-VantVeer1" target="_blank">[1]</a> on NKI mixed cohort; C: using Network 1 genes on NKI LN+ cohort; D: using van't Veer 70-gene signature <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-VantVeer1" target="_blank">[1]</a> on NKI LN+ cohort; E: using Network1 genes on NKI ER− cohort; F: using Van't Veer 70-gene signature on NKI ER− data. G: using Network 18 genes on TCGA GBM dataset; H: using 23-gene signature on TCGA GBM cohort <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-Zhang3" target="_blank">[28]</a>. I: using Network 17 genes on TCGA OV cohort. J: using 19-gene signature on TCGA OV dataset <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-Konstantinopoulos1" target="_blank">[34]</a>. Blue lines: good survival outcome group; Red lines: poor survival outcome group. LN+: lymph node positive. ER−: estrogen receptor negative.</p