Historical events have produced an ideologically charged atmosphere in the USA surrounding the potential influences of innate variation on athletic performance. We tested the hypothesis that scientific studies of the role of innate variation in athletic performance were less likely to have authors with USA addresses than addresses elsewhere because of this cultural milieu. Using scientometric data collected from 290 scientific papers published in peer-reviewed journals from 2000–2012, we compared the proportions of authors with USA addresses with those that listed addresses elsewhere that studied the relationships between athletic performance and (a) prenatal exposure to androgens, as indicated by the ratio between digits 2 and 4, and (b) the genotypes for angiotensin converting enzyme, α-actinin-3, and myostatin; traits often associated with athletic performance. Authors with USA addresses were disproportionately underrepresented on papers about the role of innate variation in athletic performance. We searched NIH and NSF databases for grant proposals solicited or funded from 2000–2012 to determine if the proportion of authors that listed USA addresses was associated with funding patterns. NIH did not solicit grant proposals designed to examine these factors in the context of athletic performance and neither NIH nor NSF funded grants designed to study these topics. We think the combined effects of a lack of government funding and the avoidance of studying controversial or non-fundable topics by USA based scientists are responsible for the observation that authors with USA addresses were underrepresented on scientific papers examining the relationships between athletic performance and innate variation. Science advances best when scientists freely pursue questions of their own choosing. However, the subjects pursued by scientists are often influenced by their cultural milieu. For example, scientific progress is hindered when the questions pursued by scientists are explicitly or implicitly influenced by government regulations or societal norms Loury . ScienceWhile government funding agencies, societal controls, and science journal editors may affect scientific inquiry into some questions, a more subtle form of the suppression of science is the self-censorship that results when scientists voluntarily avoid studying certain topics and publishing their data or ideas Loury . They maSelf-censorship is also a real phenomenon. Inbar and Lammers  presentesensu Loury , then wOur study focused on four innate traits (a) prenatal exposure to testosterone as estimated by the digit ratio  (DR) and the genotypes for the expression of the proteins (b) angiotensin-converting enzyme (ACE), (c) α-actinin-3 (ACTN3), and (d) myostatin (MSTN) that have often been associated with athletic performance (see below). Hereafter, the acronyms MSTN, ACE, and ACTN3 will be used to refer to both the genes and proteins for which they code. The evidence implicating the relationships between athletic performance and DR, ACE, ACTN3, and MSTN is summarized below.The lengths of the second  and fourth  digits are influenced by prenatal exposure to hormones. The length of 2D is positively influenced by prenatal exposure to estrogens whereas the length of 4D is positively influenced by exposure to testosterone  skeletal muscle fibers stabilizing these fibers during contraction resulting in faster, stronger contractions  are concerned that athletes and their coaches may attempt to enhance performances in strength and power dependent athletic events  by using pharmaceutical MSTN inhibitors or via “gene doping” techniques (Fedoruk and Rupert http://www.wada-ama.org).Although scientific support for an association between human athletic performance and MSTN deficiency or genotype remains limited, athletes and coaches are very interested in the effects of the genotype on performance. A Google search using the keywords “myostatin” and “strength” returned ~ 108,000 results on 30 April 2014. Because MSTN inhibits muscle growth, athlete anti-doping agencies  papers located by the Web of Science met our inclusion criteria for papers about the relationship between athletic performance and DR Table . These pAlthough the differences between the proportions were large, first authors and corresponding authors with USA addresses were as likely to be found on papers about athletic performance and DR as on those about DR independent of athletic performance Table . HoweverThe largest number of authors on papers about the relationship between athletic performance and DR listed the United Kingdom as their address Table . The proThe proportion of authors with United Kingdom addresses on papers about the relationship between DR and athletic performance was twice that of authors with USA addresses but because of small sample sizes this difference was not statistically significant Tables  and 6. TSeventy-four of the 141 (52%) papers located by the Web of Science met our inclusion criteria for papers about the relationship between athletic performance and ACE  of the 400 authors listed Table . CompareAuthors with Japanese addresses made-up the largest number of authors listed on papers about the ACE independent of athletic performance Table . HoweverForty-nine of the 94 (52%) papers located by the Web of Science met our inclusion criteria for papers about the relationship between athletic performance and ACTN3 Table . These pWhen all ACTN3 papers were considered, the proportions of first authors, corresponding authors, and all authors with USA addresses were significantly smaller on papers about athletic performance and ACE than were those found on papers about ACE independent of athletic performance Table .Authors with addresses in Spain made-up the largest number of authors listed on papers about the relationship between athletic performance and ACTN3 Table . The achFifteen of the 39 (38%) papers located by the Web of Science met our inclusion criteria for papers about the relationship between athletic performance and MSTN Table . These pAlthough the differences between the proportions were large, first authors and corresponding authors were as likely to list USA as non-USA addresses Table . HoweverAuthors with addresses in Germany represented the largest proportion of the authors listed on papers about the relationship between athletic performance and MSTN Table . No authThe largest proportion of authors on papers about MSTN independent of athletic performance listed Norway as their address Table . No authWhen all papers were pooled together for analyses  NIH funded projects examining the biomedical correlates of DR (n = 1), ACE (n = 416), ACTN3 (n = 3), and MSTN (n = 84) and NSF funded 3 projects that proposed to examine MSTN in nonhuman animals and (b) NIH funded 152 grant proposals designed to examine hypertension and race, 280 grant proposals designed to examine type 2 diabetes and race, and another 114 grant proposals that had the term “racial differences” in their titles or abstracts. No grants were funded that had the following combination of terms in their titles or abstracts: “athlete and race”, “athlete and racial differences”, “athletic performance and race”, and “athletic performance and racial differences”.http://www.grants.nih.gov/glossary). Thus, NIH did not appear to be adverse to funding studies related to sports medicine between 2000–2012 and published 3 PAs and 1 RFA soliciting proposals to study phenomena related to sports medicine, including women’s health in sports and exercise (PA-02-115) and protein interactions governing membrane transport in pulmonary health and disease . The PA most relevant to our study, PA-02-115, did not mention studying the possibility that innate factors could influence sports prowess.Between 2000–2012, NIH did not publish any Program Announcements (PA), Request for Application (RFA), or Request for Proposal (RPP) notices that solicited applications or proposals designed to directly examine the relationships between athletic performance and DR, ACE, ACTN3, or MSTN. A PA is a formal statement from NIH about a new or ongoing extramural activity or program, an RFA is a formal statement from NIH that solicits grants or cooperative agreement applications in a well-defined scientific area, and an RPP is an announcement from NIH that it wishes to award a contract to meet a specific need . Generally, authors with addresses in these countries (a) were either equally likely to be found or disproportionately more often found on papers that examined the relationship between the four innate factors and athletic performance compared to papers about the innate factors independent of athletic performance and (b) had a significantly greater proportional representation than did authors with USA addresses on papers about the relationship of the innate factors and athletic performance. However, there were several exceptions to this pattern.First, authors with addresses in Norway were never listed on papers about the relationship between MSTN and athletic performance Table . ConsequWhy didn’t scientists from USA based laboratories publish a comparable proportion of papers on the relationships between athletic performance and DR, ACE, ACTN3, or MSTN? We consider several possible explanations for this pattern.http://www.acsm.org).First, perhaps the disproportionately small representation of papers from USA based authors about the relationships between athletic performance and DR, ACE, ACTN3, or MSTN because there are relatively few sports scientists in the USA? This is not a viable explanation. There are tens of thousands of American scientists interested in sports sciences and sports medicine; 90% of the 45,000 members of the American College of Sports Medicine reside in the USA  spend large sums of money on sporting equipment and activities (http://www.census.gov/compendia/statab/cats/arts_recreation_travel/recreation_and_leisure_activities.). Collectively, these data demonstrate that the citizens of the USA devote considerable time, energy, and resources on sports activities. Even if USA scientists are less interested in sports than other Americans, we do not think that the lack of published research originating from USA based laboratories on the relationships between athletic performance and DR, ACE, ACTN3, or MSTN is primarily due to a lack of interest in sport sciences by USA scientists.Second, perhaps scientists working at USA based laboratories were simply not interested in sports so were not motivated to scientifically pursue questions about the relationships between athletic performance and innate variation in DR, ACE, ACTN3, or MSTN? This explanation is unlikely because, in general, sports interest is high in the USA. For example, Americans (a) participate in sports in large numbers  are partly responsible for the disproportionately small number of papers originating from USA based laboratories are not easily available.A lack of funding would negatively influence the pursuit of research on the relationship between athletic performance and innate factors for at least two reasons. First, contemporary scientific research is relatively expensive inhibiting scientists interested in these topics from pursuing them without financial support. Second, hiring, tenure, and promotion decisions at many academic institutions with a research mission in the USA are influenced, at least in part, by the ability of scientists to obtain funding from NIH or NSF and publish their results in peer-reviewed scientific journals thereby further inhibiting them from pursuing this kind of research.http://www.nih.gov). Although examining the biomedical correlates of DR, ACE, ACTN3, or MSTN in healthy individuals, including athletes, would appear to help meet NIH’s goals, the lack of funding for these studies may simply reflect NIH funding priorities. However, NIH did fund projects designed to examine the biomedical, but not athletic performance, correlates of DR, ACE, ACTN3, or MSTN during the sample period. Officially, NIH bases its funding decisions primarily on the scientific quality of proposals rather than targeting specific diseases but political pressure from disease advocacy organizations can have a large influence on NIH funding priorities . Since 2000, NSF has funded projects designed to examine some of the correlates of MSTN in nonhumans, but not DR, ACE, or ACTN3 (http://www.nsf.gov/awardsearch). MSTN is of special interest to scientists because elucidating its biology may help provide therapies or cures for muscular dystrophy  that effectively banned biological research on human behavior  commonly associated with athletic performance. Regardless of the reasons for this pattern and what Entine  calls “sWe searched ISI Web of Science using the keywords “athlete(s)”, “athletic performance”, “sport(s)”, “digit ratio”, “angiotensin converting enzyme”, “ACE”, “α-actinin-3”, “ACTN3”, “myostatin,” and “MSTN” in various combinations to locate papers published on these topics between 1 January 2000 and 31 December 2012. We used the ISI Web of Science to collect scientometric data because of the utility of its citation analysis tools relative to some other databases  published in English in peer-reviewed journals, (b) focused on human biology, and (c) that examined the relationship between athletic performance and each of the innate factors described above were selected for analyses. Depending on their information content, we sequentially examined the title, abstract, then when necessary, the full text of papers to determine whether or not papers met our criteria for inclusion for review. It had to be clear from the title, abstract, or text of the paper that the innate factor in question was examined in relationship to athletic performance. We excluded from analyses papers about the relationship between an innate factor and general health or exercise performance not directly in the context of athletics. Working together, we screened the studies for inclusion for review based on the criteria described above and then jointly decided whether a paper was included or excluded from analyses. The papers that remained after we removed those that did not fit our inclusion criteria made up our experimental samples.Second, we searched for papers about the innate factors not in the context of athletic performance using the NOT search function to produce our control sample. The NOT function produced lists of publications in which the innate factors were included as topics but sport(s), athlete(s), and athletic performance were not. We culled these lists of publications as described above. We used a random number generator to collect papers generated from the NOT “search” to equalize sample sizes between our experimental and control samples. For example, the search for papers about the relationship between athletic performance and ACE using the AND search function produced a list of 141 papers of which 74 met our inclusion criteria whereas the search for papers on ACE using the NOT search function produced a list of 22,031 papers. We then used a random number generator to collect 74 papers that fit our inclusion criteria in order to equalize the experimental and control group sample sizes . Authors with addresses in England, Northern Ireland, Scotland, and Wales were grouped together in the United Kingdom.To compare the patterns of publication by authors that listed USA addresses with those from other countries relative to the AND and NOT search function categories we performed three different comparisons. First, we examined the list of authors on papers found using the AND search function for the country, excluding the USA, that had the greatest number of different authors. We did this to compare the proportions of authors that listed their national address as that country on papers found using the AND and NOT search function for each of the four innate factors individually and for the factors pooled together. Second, we examined the list of authors on papers found using the NOT search function category for the country, excluding the USA, that had the most different authors. We did this to compare the proportions of authors that listed their national address as that country on papers found using the AND and NOT search function for each of the four innate factors individually and for the factors pooled together. Last, we compared the proportions of authors that listed the USA as their national address with those of authors that listed elsewhere as their national addresses found on papers using the AND and NOT search function with those of authors from the countries represented by the most authors found on papers using the AND and NOT search function for each of the four innate factors individually and for the factors pooled together. We performed these comparisons to detect whether the patterns of publication by authors with USA addresses were similar those of authors that listed their national addresses as elsewhere.Finally, we restricted ourselves to Web of Science searches using the AND or NOT search functions as described above. Restricting ourselves to these types of searches ensured that our chances of finding suitable papers for review remained consistent each time we searched.http://www.nih.gov and projectreporter.nih.gov) and National Science Foundation (http://www.nsf.gov) databases to find solicitations for new grant proposals and new grant proposals funded between 2000–2012 that contained the keywords in their project titles or abstracts. NIH and NSF are the major USA governmental funding agencies for biomedical and basic biological research, respectively.We used the keywords “athlete(s)”, “athletic performance”, “sport(s)”, “digit ratio”, “angiotensin converting enzyme”, “ACE”, “α-actinin-3”, “ACTN3”, “myostatin”, and “MSTN” in various combinations to search the US National Institutes of Health  to calculate the achieved statistical power, 1 – β = error probability (Cohen We used Fisher exact tests to test the null hypotheses that (a) authors of papers about the relationships between athletic performance and DR, ACE, ACTN3, or MSTN were as likely to have USA addresses as non-USA addresses, (b) papers about the relationships between athletic performance and DR, ACE, ACTN3, or MSTN were as likely to have originated from USA as non-USA based laboratories as determined by the national addresses their first and corresponding authors, and (c) in context of being listed on papers about the relationships between athletic performance and DR, ACE, ACTN3 or MSTN, the patterns of publication by authors that listed USA addresses were the same as those of authors that listed elsewhere as their national addresses. The Fisher exact test has no statistical rival for these sorts of comparisons (Marascuilo and McSweeney ty Cohen , of stat


Horses are much predisposed and susceptible to excessive and acute inflammatory responses that cause the recruitment and stimulation of polymorphnuclear granulocytes (PMN) together with peripheral blood mononuclear cells (PBMC) and the release of cytokines. The aim of the study is to develop easy, quick, cheap and reproducible methods for measuring tumor necrosis factor alpha (TNF-α) and interleukin-1 receptor antagonist (IL-1Ra) in the equine whole blood cultures ex-vivo time- and concentration-dependently.Horse whole blood diluted to 10, 20 and 50 % was stimulated with lipopolysaccharide (LPS), PCPwL  or equine recombinant TNF-α (erTNF-α). TNF-α and IL-1Ra were analyzed in culture supernatants, which were collected at different time points using specific enzyme-linked immunosorbent assays (ELISA). Both cytokines could be detected optimal in stimulated 20 % whole blood cultures. TNF-α and IL-1Ra releases were time-dependent but the kinetic was different between them. PCPwL-induced TNF-α and IL-1Ra release was enhanced continuously over 24–48 h, respectively. Similarly, LPS-stimulated TNF-α was at maximum at time points between 8–12 h and started to decrease thereafter, whereas IL-1Ra peaked later between 12–24 h and rather continued to accumulate over 48 h. The equine recombinant TNF-α could induce also the IL-1Ra release.Our results demonstrate that similar to PCPwL, LPS stimulated TNF-α and IL-1Ra production time-dependently in whole blood cultures, suggesting the suitability of whole blood cultures to assess the release of a variety of cytokines in health and diseases of horse. Tumor necrosis factor α (TNF-α), in addition to being cytotoxic for certain tumor cells , 2, has Moreover, it has been described that TNF-α can orchestrate the production of other cytokines such as interleukin (IL)-1, IL-6 and IL-8 to promote inflammation including increased leukocyte extravasation , 18, celThe elevated TNF-α and IL-1 levels trigger often high production of the interleukin-1 receptor antagonist (IL-1Ra), the first described naturally occurring cytokine or hormone-like molecule that functions as a specific IL-1 receptor antagonist and produced by various cell types, including mononuclear cells and neutrophils , 25. IL-Generally, in the horse, cytokine production can be influenced by several factors including various nutritional, physiological and pathological factors as well as drugs; thus, there is still much interest in measuring the concentrations of different cytokines. In man, successful ex-vivo ELISA-based method of TNF-α measurement has been well established in whole blood system , 31 whicThus, we have used the equine whole blood cultures to investigate: first, the relationship between cultures of equine whole blood dilutions and stimulated TNF-α and IL-1Ra release; second, to characterize time-concentration-responses of TNF-α and IL-1Ra production within individuals and different blood dilutions; third, effects of lipopolysaccharide (LPS), a combination of phytohemagglutinin E, concanavalin A and pokeweed mitogen (PCPwL) and equine recombinant TNF-α (erTNF-α) on TNF-α and IL-1Ra in whole blood cultures. Finally, we wished to evaluate, to our knowledge the first time, whether elevated TNF-α can enhance the IL-Ra activity in the equine whole blood cultures. Our results indicate that TNF is involved not only in the secretion of agonistic members of the cytokine network in endotoxemia but also in that of an important antagonistic member , assuming that blockade of TNF-α results in a general down-regulation of the cytokine system.For the kinetic time-course studies of TNF-α and IL-1Ra releases, blood samples were collected from three-five healthy horses of mixed breeds and 8–14 years of age. They were neither under treatment for the last three weeks before sampling nor were given immune-suppressing drugs. Animals were kept in an individual pen, fed with hey and concentrates three times a day, but had access to water ad libitum. Blood samples were collected from horses, which belong to the Large Animal Clinic of the Leipzig University.2, Penicillin G2 (100 U/ml), Streptomycin2 (100 μg/ml) and heparin2 (10 U/ml). The TNF-α-capture antibody was diluted in 0.1 M NaHCO3 solution with ultra-pure water to a working concentration of 3 μg/ml. The detection antibody was diluted to a working concentration of 250 ng/ml in PBST-buffer, containing 2 % heat inactivated  and sterile-filtered rabbit serum.7TNF-α antibodies−1) (S-Monovette).Blood samples were collected via jugular venipuncture into 9 mL vacutainer tubes with Li-Heparin (16 U × mL10 and pokeweed mitogen10) (as positive control) or with medium alone. Moreover, whole blood cultures were treated with the equine recombinant TNF-α2 atmosphere. To assess the time course of cytokine releases, aliquots of the supernatant/well were collected at time points of 0, 1, 2, 4, 6, 8, 12, 16, 24, 48 and 72 h. These aliquots were centrifuged (10000 rpm) at 4 °C for 3 min and the supernatant was separated from the cell debris and frozen at −20 °C until analysis of the cell-associated cytokines.For the stimulation of TNF-α and IL-1Ra releases, diluted blood was incubated in 24-well culture plates . Samples were stimulated with either LPSConcentrations of cytokines in whole blood cell culture supernatants were determined by enzyme-linked immunosorbent assay (ELISA) system. All samples from each blood dilutions were analyzed in one ELISA.The TNF-α protein level was measured using the equine TNF-α sandwich ELISA that was established in our laboratory. For this purpose, 96-microtiter well plates13Assay conditions were optimized by adding a series of calibrators on every assay plate that consisted of 7-point serial dilutions of equine recombinant TNF-α starting from 24.3 ng/ml to 0.033 ng/ml in duplicates in complete RPMI 1640 cell culture medium containing PBST in the presence of 10, 20 or 50 % horse serum. Also, duplicate wells of each plate contained only medium. For each ELISA plate, the optical density (OD) at 450 nm was measured, and the standard curves were established by plotting mean absorbance for each standard concentration against the target protein concentration with OD 450 nm as the Y-axis using GraphPad Prism Software program. These curves were used to determine protein expression levels.202 and 1 mM tetramethylbenzidineBasal and stimulated levels of TNF-α and IL-1Ra in whole blood culture supernatant samples were measured using ELISA. In brief, for each point of assay, 50 μl of pure cell culture supernatant were pipetted into each well of 96-microtiter well plates and diluted 1:2 by adding 50 μl PBST-buffer. Samples were then incubated for 1 h at room temperature. After three times rinsing the plates with washing buffer, 100 μl of the polyclonal biotin-labeled detecting antibody were added to each sample and incubated for further 2 h at room temperature. After three washing steps, plates were incubated with 100 μl horseradish peroxidase-conjugated streptavidin for 1 h at room temperature. After four washing steps, 100 μl of freshly prepared substrate solution  were diluted at a ratio of 1:10 and incubated. Following several washing steps, biotinylated secondary IL-1Ra antibody, streptavidin-HRP conjugate and enzyme substrate were added at different time points as given in the guidelines. Further procedures were performed analogous to the TNF-α ELISA.P value < 0.05 were considered significant.Results are presented as means ± SEM. The levels of TNF-α and IL-1Ra in whole blood supernatants were interpolated from the erTNF-α and IL-1Ra standard calibration curves which were established using GraphPad Prism 6.01The ex vivo production of TNF-α and IL-1Ra was determined in horses using heparin-displaced whole blood samples collected in a single draw. Samples were diluted 1:2 (50 %), 1:5 (20 %) and 1:10 (10 %) with complete RPMI 1640 medium. The PCPwL- but also LPS-stimulated TNF-α and IL-1Ra production was linear with different concentrations of the equine whole blood, whereby the highest cytokine concentration was achieved at whole blood dilution of 50 %. In all whole blood dilutions, PCPwL stimulated continuously TNF-α release over 24 h to the maximum and similarly but within 12 h of stimulation, LPS activated TNF-α production  Fig. -c. InterThe present study dissects equine whole blood dilution-dependent and time-course secretion of TNF-α and IL-1Ra after stimulation by PCPwL, LPS as well as erTNF-α (only IL-1Ra was stimulated) ex vivo. The decisive results were: PCPwL and also LPS triggered TNF-α and IL-1Ra releases in a blood volume-dependent manner (50 % > 20 % >10 %). In whole blood cultures, TNF-α exhibited a kinetic profile similar to that of IL-1Ra, but peaked explicitly prior to IL-1Ra activity. TNF-α peaked 8–12 h after LPS addition while the IL-1Ra level was at maximum 24 h later and remained at that level for further 24 h. The effect of several stimuli on cytokine releases was often investigated in isolated leukocytes of horses –36 and hFor reasons that the optimal stimulation conditions to assess cytokine releases in the equine whole blood cultures were not well known, the aim of the current study was to first find out which blood volumes can provide a reproducible signal-to-noise ratio of cytokine standard and sample concentration curves. In the whole blood medium, which resembles closely the physiological milieu, it was observed that the PCPwL- or LPS-stimulated TNF-α and IL-1Ra release was reduced in whole blood cultures with increasing dilutions , presumably, more than isolated PMN or PBMC cultures. The equine whole blood assay system is additionally an efficient tool to elucidate interactions of cytokines within the inflammatory responses and may provide equine veterinarians with new therapeutic approaches. Dilution of the whole blood to a ratio of 1:5 (20 %) and an incubation time of 12 h can be considered as optimal conditions to assess stimulated cytokines.BALF, Bronchoalveolar lavage fluid; ELISA, Enzyme-linked immunosorbent assay; erTNF-α, Equine recombinant tumor necrosis factor alpha; HRP, Horseradish-peroxidase; IL, Interleukin; IL-1Ra, Interleukin-1 receptor antagonist; LPS, Lipopolysaccharide; PBMC, Peripheral blood mononuclear cells; PBST, Solution containing phosphate buffered saline and Tween 20; PCPwL, Combination of phytohemagglutinin E, concanavalin A and pokeweed mitogen; PMN, Polymorphnuclear granulocytes; TMB, Tetramethylbenzidine; TNF-α, Tumor necrosis factor alpha


In vitro experiments indicated that the silencing of TNF-α in the SGC-996 cells significantly suppressed proliferation and invasion. However, apoptosis was not induced by the silencing of TNF-α. Furthermore, we traced the mechanisms underlying these effects and found that the silencing of TNF-α affected the TNF-α-AKT-NF-κB-Bcl-2 pathway in the SGC-996 cells. Our data provide evidence that autocrine TNF-α plays a role as a tumor promoter gene in gallbladder cancer cells, possibly by promoting proliferation and invasion through autocrine mechanisms.Tumor necrosis factor-α (TNF-α) has been suggested to be a putative tumor promoter gene, and autocrine of TNF-α expression has been found in colon cancer and ovarian cancer. As the role of autocrine TNF-α in human gallbladder cancer has not yet been elucidated, the present study examined the expression of TNF-α in gallbladder cancer-derived cell lines. Based on the data, TNF-α mRNA and TNF-α protein expression differed significantly different between the cell lines. In addition, using siRNA targeting TNF-α, the vector, pGPU-GFP-siTNF-α, was constructed and then transfected into the SGC-996 cells  which express high levels of endogenous TNF-α.  Gallbladder cancer is a relatively rare but highly lethal disease and is the most common cancer of the biliary tract and the seventh most common gastrointestinal carcinoma . GallblaHowever, the specific mechanisms responsible for TNF-α promoting the progression of malignant tumors have not been elucidated. Certain studies have found that TNF-α promotes a variety of inflammatory cytokines and chemokines, thus affecting the formation and development of tumor blood vessels, promoting tumor invasion and metastasis . TNF-α aHowever, there are few reports on the function of tumor-derived TNF-α. Studies on colon and ovarian cancer have found that tumor-derived TNF-α plays an important role in tumor progression ,11.et al  according to the manufacturer’s instructions. cDNA was synthesized using the AVM First Strand cDNA synthesis kit (Invitrogen). The primers for TNF-α and β-actin were synthesized according to primer design principles. TNF-α yielded a 443 bp product, and the sequences of the primers were as follows: forward, 5′-AGTGACAAGCCTGTAGCCC-3′ and reverse, 5′-GCAATGATCCCAAAGTAGACC-3′; TFN receptor 1 (TNFR1) yielded a 223 bp product, and the sequences of the primers were as follows: forward, 5′-TGCCA GGAGAAACAGAACA-3′ and reverse, 5′-AACCAA TGAAGAGGAGGGAT-3′. β-actin yielded a 254 bp product, and the sequences of the primers were as follows: forward, 5′-CTGTCTGGCGGCACCACCAT-3′ and reverse, 5′-GCAA CTAAGTCATAGTCCGC-3′. RT-PCR was performed under the following conditions: 30 cycles of denaturation at 94°C for 30 sec, annealing at 55°C for 30 sec, and extension at 72°C for 1 min fllowed by 10 min for final extension at 72°C. The data of TNF-α were normalized relative to the expression of β-actin mRNA expression in the respective samples.The cells were washed twice with cold phosphate-buffered saline (PBS) and then incubated on ice with 250 μl of RIPA buffer with 2.5 μl phenylmethylsulfonyl fluoride (PMSF) for 20 min. The cells were collected and centrifuged at 13,000 rpm for 10 min at 4°C. The protein concentrations of the cell lysates were measured in duplicate using a BCA Protein assay kit . The proportion of protein lysates and 6X loading buffer according to the ratio of 4:1 were mixed and then boiled for 5 min at 100°C. Equal amounts of total protein were resolved by sodium dodecyl sulfate (SDS 10%)-polyacrylamide gel electrophoresis and transferred onto polyvinylidene fluoride (PVDF) membranes. The PVDF membranes were then blocked with 5% non-fat milk in Tris-buffered saline with Tween-20 (TBST) for 2 h. The diluted primary antibodies, including polyclonal goat anti-human TNFR1 antibody , monoclonal mouse anti-human TNF-α (1:500) , monoclonal mouse anti-human AKT (1:500), monoclonal mouse anti-human p-AKT (1:500), monoclonal mouse anti-human nuclear factor-κB (NF-κB) (p65) (1:500), monoclonal mouse anti-human p-NF-κB (p-p65) (1:500) , monoclonal mouse anti-human Bcl-2 (1:500), monoclonal mouse anti-human Bax (1:500) and β-actin   were then incubated with the membranes overnight at 4°C. The appropriate secondary antibody conjugated with horseradish peroxidase diluted in TBST was added for 2 h at room temperature. Using a chemiluminescence western blot immunodetection kit (Invitrogen), we tested immunoreactivity according to the manufacturer’s instructions and recorded the data on hyperfine-ECL detection film. The amounts of TNF-α and TNFR1 protein were semiquantified as ratios to β-actin suggested on each gel.Suitable siRNA target sequences were found in the human TNF-α sequence. According to the design guidelines of siRNA and the literature , DNA tem6 cells/well with 4 ml of DMEM medium supplemented with 10% FBS. The amount of autocrine TNF-α in the cells was determined after 1, 2, 3, 4 and 5 days by ELISA. ELISA was performed to detect TNF-α in the cell culture supernatants of the untransfected SGC-996 cells, the TNF-α siRNA-transfected SGC-996 cells and the SGC-996NC-transfected cells using a TNF-α (H) ELISA kit . Two hundred microliters of supernatant were added to each well. ELISA was perforemd according to the manufacturer’s instructions. The sensitivity of the assays was 7.8 pg/ml. The absorbance was detected at 450 nm. Each plate test was repeated 3 times.To analyze autocrine TNF-α in the untransfected SGC-996 cells, TNF-α small interfering (siRNA)-transfected SGC-996 cells and the SGC-996 negative control (NC)-transfected cells, these cells were seeded into plates at a density of 3×103 cells/well with 100 μl of DMEM medium supplemented with 10% FBS. The proliferative activity was determined after 1, 2, 3, 4 and 5 days by the addition of 10 μl of sterile 3--2,5-diphenyltetrazolium bromide (MTT)  to each well. The reaction was terminated after 4 h of incubation at 37°C by the addition of 100 μl of dimethyl sulfoxide . The optical density (OD) value was obtained by measuring absorbance at a wavelength of 570 nm. Each well test was repeated 6 times.To analyze cell proliferation, the untransfected SGC-996, TNF-α siRNA-transfected SGC-996 and SGC-996NC-transfected cells were seeded into 96-well plates at a density of 105) in 200 μl of DMEM medium with 2.5% FBS were placed in the upper chamber. The lower chamber was filled with 700 μl DMEM medium with 10% FBS as the chemoattractant. The migration chamber was incubated for 8 h at 37°C and 5% CO2. The cells on the upper surface of the membrane were removed by gentle scrubbing with a cotton swab. Membranes were fixed in a stationary liquid of 95% ethanol and 5% acetic acid for 30 min and stained with hematoxylin and eosin (H&E). The number of cells on the lower surface of the membrane in 5 random visual fields (×400) was then counted using a bright field light microscope. Each assay was repeated in triplicate.Cell motility was assayed using Transwells (24-well format) with 8 μm pore polycarbonate membranes . The lower side of the membranes was covered with 5 μg fibronectin (BD Biosciences). To test cell motility induced by TNF-α siRNA, the treated or untreated SGC-996 cells  with 8 μm polycarbonate membranes (BD Biosciences) were used. Briefly, the upper side of the membranes was coated with Matrigel matrix (20 μg/well) and the membranes were then air-dried for 1 h of incubation 37°C. The lower side of the membranes was coated with 5 μg fibronectin (BD Biosciences). Other experimental procedures were the same as those for the migration assay.5/well) in 6-well plates in DMEM medium with 10% FBS for 48 h to collect the cells and stained using the Annexin V-PE/7-aminoactinomycin D kit  according the manufacturer’s instructions, and analyzed using a Becton-Dickinson FACSCalibur.To determine the apoptosis induced by TNF-α siRNA, the treated or untreated SGC-996 cells were seeded . A P-value <0.05 was considered to indicate a statistically significant difference.We analyzed the mRNA expression of TNF-α and TNFR1 in the NOZ and SGC-996 cells. Using RT-PCR, we detected the mRNA expression of TNF-α and TNFR1 in both cell lines . The TNFWe then determined the TNFR1 and TNF-α protein expression in the NOZ and SGC-996 cells by western blot analysis. As expected, the protein expression of TNFR1 and TNF-α was detected in both cell lines. We observed no difference in the TNFR1 protein expression levels in the 2 cell lines by western blot analysis ; howeverWe used an RNAi-mediated method to silence TNF-α in order to characterize the biological effects of TNF-α in the SGC-996 cells. The DNA sequencing results verified that TNF-α siRNA plasmid construction was successful. We obtained stably transfected cells using G418 after 2 weeks, as shown by a screening test. RT-PCR revealed a decrease in TNF-α mRNA levels in the siRNA-transfected cells, while the levels of the β-actin gene maintained relatively unaltered and mock transfection or transfection with the C-N/siRNA vector had no effect on TNF-α mRNA expression . We obtaWe then analyzed the autocrine TNF-α protein levels in all cell culture supernatants of the untransfected SGC-996, TNF-α siRNA-transfected SGC-996 and SGC-996NC-transfected cells . Consistin vitro cell proliferation assay indicated that the growth of the cells in the TNF-α siRNA-transfected group was markedly reduced compared with the untreated group, which was the same as the growth of the C-N/siRNA-transfected group. These data support the autocrine role of TNF-α in affecting the proliferaton of gallbladder cancer cells.To determine whether the endogenous TNF-α promotes cancer cell proliferation, we first treated the human SGC-996 cells with siRNA directed against TNF-α. We transfected the SGC-996 cells with TNF-α siRNA to induce the downregulation of TNF-α gene expression with C-N/siRNA and mock-treated groups were used as controls. We examined cell proliferation at 1, 2, 3, 4 and 5 days by MTT assay. Compared to the C-N/siRNA-transfected cells and the untransfected cells, cell proliferation in the TNF-α siRNA-transfected group was slower (P<0.05) . The datTo determine whether the migration and invasiveness of the SGC-996 cells depends on endogenously secreted TNF-α, we used Transwell assay to examine the effects of the knockdown of the TNF-α gene in the SGC-996 cells. Following staining with H&E, 5 different fields  were counted to test the numbers of migrated and invaded cells. The total number of cells in the TNF-α siRNA group that migrated and invaded through the Transwell polycarbonate filter was significantly lower than that of the cells in the SGC-996 (untreated) group  and 8  after the cells were stained with Annexin V-PE/7-aminoactinomycin D. The untransfected SGC-996, TNF-α siRNA-transfected SGC-996 cells and the SGC-996NC-transfected cells exhibited a similar rate of apoptosis .To investigate the mechanisms of TNF-α silencing responsible for the decrease in growth and invasion, we assessed the changes in AKT, p-AKT, NF-κB (p65), p-NF-κB (p-p65), Bcl-2 and Bax protein levels. These proteins are vital to the survival of gallbladder cancer cells. The western blot analysis results indicated that the AKT, p-AKT, NF-κB (p65), p-NF-κB (p-p65) and Bcl-2 protein levels in the SGC-996si cells decreased (P<0.05) in comparison to the SGC-996NC-transfected and untransfected SGC-996 cells. We also examined the expression of the Bax gene, which can promote the apoptosis of gallbladder cancer cells, and found that TNF-α silencing did not significantly increase the expression of the Bax gene P>0.05 .In 1975, Carswell found a factor that can rapidly cause hemorrhaging and tumor necrosis, named TNF. Thus, TNF was initially identified and named as such, as it can cause tumor necrosis . TNF-α, To our knowledge, this study is the first to demonstrate the role of tumor-derived TNF-α in promoting the invasion and proliferation of human gallbladder cancer cells. We further investigated the possible mechanisms underlying this process. We determined whether TNF-α was expressed in two different gallbladder cancer cell lines (NOZ and SGC-996). We found that the mRNA and protein expression levels of TNF-α in the SGC-996 cells were higher compared to the levels in the NOZ cells. Thus, on the basis of our findings and those of previous studies ,11,23,24in vitro cell proliferation assay indicated that the growth of the cells in the TNF-α siRNA group was markedly reduced compared with the untreated group and the C-N/siRNA group. This result is different from that of a previous study on ovarian cancer, in which the authors concluded that TNF-α RNAi cells grew at a similar rate to normal cells . In the present study, to determine whether the suppressive effects of TNF-α gene silencing on cell viability were due to apoptosis, we employed flow cytometry after the cells were stained with red fluorescence Annexin V-PE/7-aminoactinomycin D. The untransfected SGC-996, TNF-α siRNA-transfected SGC-996 and SGC-996NC-transfected cells exhibited a similar rate of apoptosis. Further studies are required to elucidate the specific mechanisms responsible for this phenomenon.A number of studies have reported that TNF-α can induce the expression of MMPs, interleukin (IL)-8, CXC chemokine receptor type 4 (CXCR), VEGF and MCP-1, thus enhancing tumor cell invasion and metastasis ,18,20. OIn this study, we assessed the changes in AKT, p-AKT, NF-κB (p65), p-NF-κB (p-p65), Bcl-2 and Bax protein expression. These proteins are vital to the survival of gallbladder cancer cells. The western blot analysis results indicated that AKT, p-AKT, NF-κB (p65), p-NF-κB (p-p65) and Bcl-2 protein levels in the SGC-996si cells were decreased compared with those in the SGC-996NC-transfected and untransfected SGC-996 cells. We also examined the expression of Bax, which can promote the apoptosis of gallbladder cancer cells, and found that TNF-α silencing did not significantly increase the expression of the Bax gene.in vitro. This provides further evidence that targeting TNF-α and its intracellular pathways may prove useful in the treatment of gallbladder cancer.In conclusion, in the present study, we verify the biological behavior of gallbladder cancer cell-derived TNF-α. We provide evidence that the reduction of autocrine TNF-α in gallbladder cancer cells can exert inhibitory effects on the ability of the cells to grow and migrate


Helicobacter pylori infection can induce a gastric mucosal inflammatory response that may be influenced by -308 (G > A) polymorphisms and gene expression of the TNF-α gene.Tumor necrosis factor plays a critical role in the pathogenesis of gastric diseases such as gastric cancer, and an abnormal inflammatory response has frequently been observed in dyspeptic patients. TNF-α was characterized using polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP). The expression level was measured using real-time PCR, and relative quantification (RQ) was calculated using the comparative CT method (2-ΔΔCT).One hundred and thirty-four gastric biopsy samples were collected from patients of both genders  with gastric symptoms. The -308 (G > A) polymorphism of TNF-α gene expression in patients with gastritis; on the other hand, no statistical differences were observed in patients with gastric cancer. In addition, no association was found among -308 polymorphism genotypes, virulence markers, or TNF-α gene expression.The analysis revealed an increase in Helicobacter pylori induces a large increase in TNF-α expression in patients with gastritis, regardless of tissue inflammation, but after the tissue becomes neoplastic, the presence of bacteria did not influence expression. These results suggest that the TNF-α pathway may play an important role in the progression from gastritis to gastric cancer Helicobacter pylori (H. pylori) is a gram-negative bacterium that colonizes more than half of the world population, causing chronic infection. It comprises a major risk factor for gastric cancer (GC), which is the fourth most commonly diagnosed cancer and the second most deadly one, with over 750,000 new cases annually worldwide [orldwide , 2.H. pylori, such as the genes cagA, vacA and dupA, and host genetic factors. However, bacterial factors or host genetic factors alone are not sufficient to explain why so few individuals harboring H. pylori develop gastric disease [Several factors are correlated with the severity of clinical outcomes. These factors include the virulence markers of  disease , 4.H. pylori infection of gastric mucosa is the induction of a gastric mucosal inflammatory response. This reaction is induced by the contact of H. pylori with gastric cells, and is followed by neutrophil and mononuclear cell activation and the transcription and synthesis of pro-inflammatory and anti-inflammatory cytokines [Evidence suggests that the main pathophysiological event in the ytokines , 5.Tumor necrosis factor alpha (TNF-α) is a very important multifunctional pro-inflammatory cytokine. It provides a rapid form of host defense against infection by gram-negative bacteria. It is produced by activated monocytes and macrophages, which play a key role in inflammatory response . HoweverTNF-α gene have been associated with a variety of inflammatory diseases and cancer [TNF-α-308 G > A (rs1800620), one of the most frequently studied polymorphisms on this gene, seems to have an influence on transcriptional activities and may contribute to the development of gastric disease [H. pylori virulence markers, such as cagA, vacA, and dupA genes, play important roles in gastric mucosal inflammation and injury through activated inflammatory cell infiltration [Several polymorphisms in the promoter region of the d cancer , 10. TNF disease . Furtherltration –14.CagA-cytotoxin is a virulence factor that has been well established as being associated with the development of peptic ulceration or gastric adenocarcinoma. This protein is translocated into the epithelial cytosol, thus causing changes that favor the persistence of the bacterium, but which also lead to changes in gastric cells without the direct adherence of bacteria in the stomach .vacA), which encodes the secreted toxin VacA. The VacA toxin exhibits multiple cellular activities, and all H. pylori strains possess a vacA gene. This gene displays allelic diversity in two main regions: the signal (s) region (s1 and s2) and the middle (m) region (m1 and m2). Different combinations are associated with vacuolating capabilities. In addition, vacA s1/m1 strains are correlated with greater vacuolating activity and gastric disease, while vacA s2/m2 strains appear to be nonvacuolating [Another important virulence marker is vacuolating cytotoxin A  in Bauru, São Paulo, Brazil (under registration n. 068/12). Written informed consent was obtained from every patient involved in the study. All tissue samples were obtained by means of endoscopy or gastric surgery, procedures which were performed in the Department of Gastroenterology at Marília Medical School, the São Paulo Hospital, or the João de Barros Barreto University Hospital.One hundred and thirty-four gastric biopsy samples were collected from patients of both genders  with gastric symptoms. Histological parameters were analyzed and revealed 70 patients with chronic gastritis (G), 40 with normal gastric tissue (C), and 24 with gastric cancer (GC) tissue. The histological parameters were classified according to the Sydney system  and LaurH. pylori and virulence markers from cagA, vacA and dupA genes were all detected using polymerase chain reaction, as in previous studies [DNA from gastric biopsies was extracted using the QIAamp® tissue kit  according to the manufacturer’s recommendations. The presence of  studies –27.TNF-α was characterized using both polymerase chain reaction (PCR) and the restriction fragment length polymorphism (RFLP) technique according to the Wilson’s protocol [5′AGGCAATAGGTTTTGAGGGCCAT3′ and 5′TCCTCCCTGCTCCGATTCCG3′, and the amplification condition that was utilized included 35 cycles: 45 s at 94 °C, 45 s at 61 °C, and 45 s at 72 °C. The amplicons were digested with NcoI  overnight at 37 °C, visualized in 3 % agarose gel, stained with ethidium bromide, and analyzed using an Alpha Imager 2200 (Alpha Innotech Corporation™). The fragments visualized were 107 bp in length in the case of allele A and 87 and 20 bp in length in the case of allele G.The -308 (G > A) polymorphism of protocol .The 107 After collection, the biopsy fragments were immediately placed into an RNAlater solution  and stored at -20 °C until use. Total RNA was isolated using an RNeasy Mini Kit , and RNA concentration and quality were measured using the NanoDrop 2000 Spectrophotometer . The concentration was adjusted to 500 ng, and cDNA synthesis was conducted using High-Capacity cDNA Reverse Transcription Kits  following the manufacturer’s instructions.TNF-α gene expression (Hs01113624_g1), the normal gastric tissue sample, which was negative for H. pylori infection, was designated as a calibrator. All reactions were repeated, and GUSB (Hs00187320_m1), UBC (Hs00221499_m1), and TBP (Hs00187332_m1) were used as reference genes.In order to determine -ΔΔct).The expression level was measured using the TaqMan® System  within the Applied Biosystems 7500 Fast Real-Time PCR system . Relative quantification (RQ) was calculated using the comparative CT method  were calculated using a dominant model . To analyze gene expression, the two-tailed Student’s t-test was used. Statistical analyses were performed using the GraphPad Prism 5 statistical package. Statistical significance was accepted at p < 0.05.The H. pylori infection and 63 (47 %) of whom tested negative for H. pylori infection. H. pylori presence was associated with the development of G and GC (p < 0.001), and an association between cagA and s1/m1 alleles of the vacA gene in samples from G and GC was observed (p < 0.001).Table H. pylori was excluded due to the low sample number.For the other analyses, the group with gastric cancer tissue that also tested negative for p = 0.076), genotype distributions were not within the Hardy-Weinberg equilibrium in the gastric cancer group (p = 0.012) or in the control group (p = 0.0065). Statistical analysis did not detect an association between the polymorphisms and gastric disease or between the polymorphisms and the presence of H. pylori.Genotype and allele frequencies are presented in Table H. pylori infection (control group RQ = 1.072).It is important to note that, in the expression analysis, samples from all of the test groups were compared to the normal gastric tissue samples, which were negative for p = 0.0001). In the second analysis, the gastritis group was divided according to the presence of H. pylori: TNF-α expression was significantly higher in patients with gastritis who had tested positive for H. pylori    in relation to the control group (p = 0.6332) did not present any alterations  was compared to patients with gastric cancer. Expression in neoplastic tissue  polymorphism and TNF-α gene expression in any of thesamples. First, gene expression of the samples withG/G genotypes was compared to that ofG > A + A/A genotypes in patients with gastritis (p = 0.8269) and with gastric cancer (p = ns).The small number of A/A genotypes,particularly in the gastric cancer samples, may have influenced theanalysis. In subsequent analyses, gene expression was compared tothe presence of cagA gene (p = 0.4677), to the presence of thedupA gene (p = 0.0518), and to genotypes s1/m1 and s2/m2 of the vacA gene (p = 0.3578). The presence of more pathogenic of H. pyloristrains did not induce changes in TNF-α expression. In addition, cagA, vacA and dupA genes were not found to be associated with differential gene expression of TNF-α cytokine.No association was observed between the genotypes or alleles of the -H. pylori infection in gastric mucosa causes an inflammatory response in the host characterized by infiltration of immune cells such as macrophages, neutrophils, and lymphocytes. This reaction is most likely induced by the contact of the bacterium with the epithelium; however, it has been estimated that only 20 % is adhered to the gastric mucosa and therefore able to activate the inflammatory response [response .Thus, it is evident that virulence factors play a more important role in the progression of gastric carcinogenesis than the mere presence of the bacteria does.TNF-α cytokine plays an essential role in host immunity against infectious disease and cancer progression [TNF-α-308 G > A polymorphisms and gastric carcinogenesis are very controversial [The gression . Howeveroversial –35.TNF-α-308 G > A polymorphism in different ethnic populations. In Caucasian populations, they demonstrated a weak association between the TNF-α-308 A variant and gastric cancer risk. Meanwhile, no associations were found between this A variant and gastric carcinogenesis in East Asian samples or other ethnic populations, including the Brazilian population. Two of the most recent Brazilian articles cited in this meta-analysis evaluated patients with chronic gastritis and gastric cancer and did not find any association between the TNF-α-308 A polymorphism and gastric carcinogenesis [Li et al.  evaluateogenesis , 36.TNF-α mRNA was overexpressed in relation to the control patients, and the association was greater among patients who had gastritis and were also H. pylori positive. On the other hand, there was no increased expression in patients with gastric cancer. These results indicate the involvement of this cytokine in initial carcinogenesis. A recent study with a mouse model showed that TNF-α is also produced in excess in early colonic lesions in cases of colorectal cancer, and the data supported the closest association between TNF-α and early tumor progression [The association between inflammation and disease progression is well described in a gastric cancer model, which begins with chronic gastritis that persists and may induce changes such as metaplasia, dysplasia and, finally, cancer . We alsogression .cagA-positive strains of H. pylori had the highest cytokine mRNA and protein levels. However, we did not find an association between higher cytokine mRNA level and patients infected by cagA-positive strains. Santos et al. [TNF-α-308 G > A polymorphism and infection by cagA-positive strains. This polymorphism apparently does not affect the presence of H. pylori or the progression of gastric disease.Bartchewsky et al.  showed ts et al.  found siH. pylori genotypes and their effects on cytokine release levels. These authors studied five genotypes: cagA and vacA s1m2 (genotype 1); cagA and vacA s1m1 (genotype 2); cagA, vacA s1m2, and babA2 (genotype 3); vacA s2m2 (genotype 4); and cagA and vacA s2m2 (genotype 5). These researchers found that all of these genotypes increased IL-1β, IL-6, IL-8, IL 10, and TNF-α levels in THP-1 cells. However, genotype 5 was found to cause higher amounts of IL-1β, IL-6, TNF-α, and IL-10, whereas genotype 1 induced the highest levels of IL-8. Thus, they suggested that the host cytokine response varies depending on the individual specific immune response to different H. pylori strains.More recently, Caliskan et al.  performeH. pylori-infected children and uninfected children and found high mRNA expression of IL-6, IL-10, IFN-γ, and TNF-α. However, H. pylori cagA-positive strains were associated only with higher IL-6 and IL-10 mRNA expression in relation to the cagA-negative strains. Meanwhile, TNF-α presented increased protein levels and was correlated with bacterial density.In another recent study, Michalkiewicz et al.  analyzedH. pylori genotypes, stimulates mRNA synthesis of TNF-α. It is also possible that an increase in protein levels occurs without a significant increase in gene expression. The important finding is that, regardless of the pathway, TNF-α induces an intense inflammatory response that inhibits gastric acid production and suggests a worse prognosis.Our results are consistent with those of several authors, and we offer two hypotheses. It is possible that bacterial density, not the TNF-α-308 G > A polymorphisms did not influence TNF-α mRNA expression in cases of gastric disease. In patients with postoperative sepsis, Baghel et al. [TNF-α cytokines than other genotypes. Higuchi et al. [TNF-α-308 polymorphism presented higher transcriptional activity and increased TNF-α cytokine production. Hosono et al. [TNF-α expression did not differ significantly.In addition, we found that, contrary to results reported in the literature, l et al.  revealedi et al.  reportedo et al.  found inTNF-α, which are not necessarily associated with the mRNA levels of TNF-α. A recent study published by de Oliveira et al. [TNF-α (-308) did not have any effect on TNF-α or IL-10 expression levels. It is important to note that this study was performed on a Brazilian population and reinforces the hypothesis mentioned above.These contradictory results may be explained, at least in part, by the fact that the Brazilian population is composed of a genetic miscegenation of various ethnic groups. This variation may influence allele and/or genotype frequencies and their correlations with gene expression. Furthermore, most studies evaluate protein levels of a et al.  found reHelicobacter pylori induces a large increase in TNF-α expression in patients with gastritis, regardless of tissue inflammation, but after the tissue becomes neoplastic, the presence of bacteria did not influence expression. These results suggest that the TNF-α pathway may play an important role in the progression from gastritis to gastric cancer.Ours results show that the presence of This study was approved by the Research Ethics Committee of Sacred Heart University (Universidade do Sagrado Coração) in Bauru, SP, Brazil (under registration n. 068/12).Written informed consent was obtained from all patients or their legal guardians for publication of this case report.


In contrast, the expression of pro‐apoptotic genes, including CASP8, FADD and TNFRSF1A and TNFRSF1B, were significantly down‐regulated following TNF‐α treatment. These changes in mRNA levels were paralleled by decreases in protein levels of caspases 8 and 10, TRADD, FADD, TNFRSF1A and TNFRSF1B, and increased cFLIP protein levels, as detected by western blotting. These data indicate that when neutrophils are triggered by TNF‐α exposure, they undergo molecular changes in transcriptional expression to up‐regulate expression of specific anti‐apoptotic proteins and concomitantly decrease expression of specific proteins involved in death receptor signaling which will alter their function in TNF‐α rich environments.Responses of human neutrophils to TNF‐α are complex and multifactorial. Exposure of human neutrophils to TNF‐α in vitro primes the respiratory burst, delays apoptosis and induces the expression of several genes including chemokines, and TNF‐α itself. This study aimed to determine the impact of TNF‐α exposure on the expression of neutrophil genes and proteins that regulate apoptosis. Quantitative PCR and RNA‐Seq, identified changes in expression of several apoptosis regulating genes in response to TNF‐α exposure. Up‐regulated genes included TNF‐α itself, and several anti‐apoptotic genes, including BCL2A1, CFLAR (cFLIP) and TNFAIP3, whose mRNA levels increased above control values by between 4‐20 fold ( TNF‐α is a pleiotropic cytokine that regulates the activity of many immune cells and tissues. It plays an important role in a number of physiological processes, such as cellular differentiation, but is also implicated in many pathological processes The effects of TNF‐α on target cells are varied, and activation is mediated by the surface receptors, TNFR1  and TNFR2 . Ligation of these receptors can lead to activation of a range of cellular responses, including cell survival, apoptosis, differentiation and proliferation Neutrophils play a central role in TNF‐α signaling in immune activation during inflammation, by both generating TNF‐α and responding to it to become activated Neutrophil responses to TNF‐α are complex and varied in that low concentrations (∼10 ng/mL) can “prime” the respiratory burst, up‐regulate surface receptors, stimulate adhesion and delay apoptosis, whereas higher concentrations (>30 ng/mL) can promote apoptosis The aim of this study was to determine the molecular events that regulate neutrophil responses to TNF‐α signaling. We demonstrate that in response to low concentrations of TNF‐α, neutrophils are activated to express TNF‐α itself, exhibit delayed apoptosis and in parallel, down‐regulate expression of a number of genes and proteins that mediate death‐receptor signaling. These processes could therefore contribute to the fine tuning of responses of human neutrophils to TNF‐α in inflammatory conditions.The following reagents were used in this study: Polymorphprep (Axis‐Shield); RPMI 1640 media ; Rapid Romanowsky stain ; human AB serum and propidium iodide ; TRIZOL, Superscript III First Strand cDNA Synthesis kit and FITC‐Annexin V (Invitrogen); RNAeasy kit, DNase and SYBR Green PCR kit (Qiagen); polyvinylidenedifluoride (PVDF) membranes and Immobilon Western Chemiluminescent HRP Substrate (Millipore). Primary antibodies  were from Cell Signaling while anti‐actin was from Abcam. Secondary antibodies  were anti‐mouse IgG (Sigma) and anti‐rabbit IgG .6/mL. Neutrophil purity was assessed by staining with Rapid Romanowsky stain and was >97%. Re‐suspended neutrophils were incubated up to 6 h or overnight, with RPMI 1640 media at 37°C in a 5% CO2 incubator, with or without 10% (v/v) human AB serum, with and without TNF‐α, as indicated in the text.This study was approved by the University of Liverpool Committee for Research Ethics. Neutrophils were isolated from heparinised peripheral blood of healthy donors using Polymorphprep, according to the manufacturers instructions. Red blood cell contamination was removed by hypotonic lysis. Neutrophils were re‐suspended in RPMI 1640 media to a concentration of 102 Profiler PCR Array (SA Biosciences) was used to detect neutrophil transcripts whose levels were regulated by TNF‐α treatment. Thereafter, specific primers for genes of interest were purchased from PrimerDesign and Eurofins (Table After incubation in the absence or presence of TNF‐α 10 ng/mL), neutrophils were pelleted, and RNA from the pellet was extracted with Trizol reagent using an RNAeasy kit, which included a DNA digestion step. Total RNA concentration and integrity was assessed using the Agilent 2100 Bioanalyser RNA Nano chip. cDNA was synthesized from 200 ng of total RNA from each sample, using the Superscript III First Strand cDNA Synthesis kit, in a 20 μL reaction volume, as per the manufacturer's instructions. Quantitative PCR analysis was carried out with the SYBR Green PCR kit, as per the manufacturer's instructions, using a Roche 480 LightCycler in a 96‐well plate using 1 μL of the above cDNA suspension in a 20 µL reaction volume. Target gene expression was quantified against a panel of housekeeping genes . Initially, a Human Apoptosis RT0 ng/mL, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40548).Total RNA was enriched for mRNA using poly‐A selection, and standard Illumina protocols were used to generate 50bp single‐end read libraries, which were analyzed on a HiSeq 2000 instrument. Reads were mapped to the human genome (hg19) using TopHat v1.4.1 www.ingenuity.com).Bioinformatics analysis was carried out using Ingenuity Pathway Analysis , and gel electrophoresis was run for 1 h. Separated proteins were transferred to polyvinylidenedifluoride (PVDF) membranes by electrophoresis for 80 min. The membranes were blocked with 5% non‐fat skimmed milk for 1h, incubated with primary antibodies and then incubated overnight with gentle agitation at 4°C. Primary antibodies were: rabbit anti‐human TNFR1 (TNFRSF1A), TNFR2 (TNFRSF1B), TRADD, FADD, Caspase‐10, FLIP (CFLAR) or mouse anti‐human Caspase‐8. After incubation with appropriate secondary antibodies, detection was by Immobilon Western Chemiluminescent HRP Substrate and careful exposure to film to avoid signal saturation. The membranes were then stripped for 30 min using stripping buffer  Tween‐20, HCl to pH 2.5), then re‐blocked and incubated with a monoclonal mouse anti‐actin antibody followed by anti‐mouse IgG secondary antibody. Band intensity was measured as an integrated density value, as determined by AQM Advance 6 software.Isolated neutrophils were pelleted and washed with Phosphate Buffered Saline and lysed with boiling Laemmli lysis buffer at a concentration of 104) were stained with Annexin V‐FITC for 15 min (to measure early apoptosis) and then incubated with propidium iodide (1 µg/mL) (to detect changes in membrane permeability in late apoptotic cells). Stained cells were then analyzed on a Guava EasyCyte flow cytometer. Five thousand events were analyzed per sample.After incubation in the absence or presence of TNF‐α (10 ng/mL), neutrophils , confirming previous observations  Incubation of human neutrophils with 10 ng/mL of TNF‐α resulted in delayed apoptosis, as determined by decreased annexin V binding (due to surface phosphatidylserine exposure). Previous work has shown that this cell death is caspase dependent http://www.sabiosciences.com/rt_pcr_product/HTML/PAHS-012Z.html for a full list of genes). Of these 84 genes, 13 were selected for further analysis  in which the fold change in gene expression values between the control (untreated) samples and the TNF‐α treated samples were projected onto the Death Receptor Signaling pathway. In this scheme, mRNA levels that decreased following TNF‐α treatment are shown in green, while those whose levels increased are shown in red. This scheme predicts that signaling of apoptosis via a range of death receptors would be down‐regulated as a result of the TNF‐α treatment of neutrophils.Bioinformatics analyses of the changes in gene expression following stimulation with TNF‐α for 1 h are shown in Figure The above transcriptome analyses were performed after 1 h incubation in the presence and absence of TNF‐α used at 10 ng/mL. In order to determine the kinetics of mRNA expression in response to TNF‐α stimulation, neutrophils were incubated for up to 6 h with TNF‐α and changes in gene expression were determined by qPCR. Maximal changes in gene expression were detected by 1 h incubation with TNF‐α, and extension of the incubation time to either 3 or 6 h did not result in any changes in expression of those genes of interest, above those observed after 1 h incubation , while the expression of all other proteins investigated significantly decreased following TNF‐α treatment (P < 0.05). Thus, levels of expression of TNFR1, TNFR2, TRADD, and FADD were all significantly decreased after treatment. The antibody that we used in these studies detects both the pro‐ (inactive) and the cleaved (activated) forms of caspase 8 . No significant changes in protein levels were detected by Western blotting of protein lysates prepared following 2 h incubation (data not shown), but for most proteins under investigation maximal changes in expression were detected by 4–6 h incubation (data not shown). Figure This data presented in this study describe a potential new mechanism by which human neutrophils control their responsiveness to TNF‐α via up‐regulation of the expression of anti‐apoptotic genes and the simultaneous down‐regulation of expression of genes involved in death‐receptor signaling. Such a mechanism would be predicted to enable neutrophils to survive and maintain their functions within TNF‐α‐rich environments during inflammation. For example, in rheumatoid arthritis TNF‐α plays a central role in pathology and neutrophil function is activated in the blood of untreated patients de novo gene expression is also important in the modulation of neutrophil responses to TNF‐α, by down‐regulation of key proteins that are involved in TNF‐α mediated apoptosis. Such a mechanism may explain limitation of neutrophil responses to either exogenous or autocrine TNF‐α.The effects of TNF‐α on human neutrophils are complex and varied, and are reported to be both concentration‐ and time‐dependent, as well as having differential effects on different sub‐populations of cells Most studies of neutrophil gene expression have, to date, focused upon identifying those genes whose expression is elevated or activated following inflammatory challenge. Few studies have reported decreased gene expression following activation, despite the fact that down‐regulation of key proteins can have a profound effect on neutrophil function and their subsequent contribution to infectious or inflammatory challenge. Here, we show significant down‐regulation of a number of genes that control responsiveness to death signals. The mechanisms responsible for decreased expression of particular genes are not known, but may involve activation of transcriptional repressors or chromatin re‐modelling, such as increased methylation or de‐acetylation of chromatin. While changes in mRNA levels were maximal 1 h after stimulation with TNF‐α, incubation periods in excess of 4 h were required before corresponding changes in protein levels were detected. This time delay between a decrease in mRNA and a decrease in protein levels, would allow an appropriate time window for the neutrophil to positively respond to the cytokine and then express new proteins, in this case anti‐apoptotic proteins, that alter cell function. There was some variability in the time after incubation with TNF‐α before the changes in protein levels were significantly different: some detectable by 4 h of incubation, others only detectable after 6 h incubation. This is possibly a consequence of differences in the turnover rates of these different proteins.n = 6), that were performed with a different set of donors as those used for RNA‐Seq. Hence, this difference may represent donor variation.Using both quantitative PCR and RNA‐Seq to measure transcript levels in neutrophils following TNF‐α treatment, we demonstrated a very close correlation between the sets of data generated by these two independent methods. The former method is commonly‐used to measure expression levels of specific transcripts in human neutrophils, but the latter has not been used extensively to quantify the transcriptome of these cells. The benefits of RNA‐Seq over other transcriptome methods  are significant n = 4) This study demonstrated that several genes involved in NF‐κB signaling pathway were up‐regulated .There are no financial or commercial conflicts of interest.Additional supporting information may be found in the online version of this article at the publisher's web‐siteFigure LegendsClick here for additional data file.Figure S1. TNF‐α delay of neutrophil apoptosis.Click here for additional data file.Figure S2. Previously published RNA‐Seq data deposited in the NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE40548 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40548), were analyzed for relative expression levels of the listed receptors (A) or metalloproteinases (B).Click here for additional data file.


MAP4 and FLT3, both of which included a common variant with a large effect. However, SKAT with all 3 weighting schemes performed poorly. Overall power across all causal genes was about 0.05, which was almost identical to the type I error rate. This poor performance is partly due to a small sample size because of the need to analyze only unrelated individuals. Because a half of causal SNPs were not found in the annotation file based on the 1000 Genomes Project, we suspect that performance was also affected by our use of incomplete annotation information.Sequence kernel association test (SKAT) has become one of the most commonly used nonburden tests for analyzing rare variants. Performance of burden tests depends on the weighting of rare and common variants when collapsing them in a genomic region. Using the systolic and diastolic blood pressure phenotypes of 142 unrelated individuals in the Genetic Analysis Workshop 18 data, we investigated whether performance of SKAT also depends on the weighting scheme. We analyzed the entire sequencing data for all 200 replications using 3 weighting schemes: equal weighting, Madsen-Browning weighting, and SKAT default linear weighting. We considered two options: all single-nucleotide polymorphisms (SNPs) and only low-frequency SNPs. A SKAT default weighting scheme (which heavily downweights common variants) performed better for the genes in which causal SNPs are mostly rare. This SKAT default weighting scheme behaved similarly to other weighting schemes after eliminating all common SNPs. In contrast, the equal weighting scheme performed the best for  Because rare variants are expected to have larger effect sizes than common variants, they may explain a proportion of the missing heritability . As reviet al , becauseet al [et al [et al [Because these burden tests implicitly assume that all variants influence the trait in the same direction and with the same magnitude of effect (after incorporating known weights), their power is reduced if certain variants confer increased risk while others are protective . Severalet al  quantifyl [et al  uses thel [et al . The seql [et al  is a scoPerformance of burden tests has been shown to depend on how rare and common variants are weighted when collapsing them in a genomic region ,7. In thGAW18 provided whole genome sequencing (WGS) data and longitudinal phenotype data for related individuals of Mexican American heritage. Because SKAT can only handle unrelated individuals, we used both sequence and phenotype data on a subset of 142 unrelated individuals. Using PLINK [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G-2010-06.html). Of the 8,348,674 single-nucleotide polymorphisms (SNPs) in the data set, 2,652,577 were located in genes, with a total of 10,922 genes. Some of these SNPs belonged to multiple overlapping genes and were used for multiple genes in which they were contained. Because 1,856,005 SNPs in the genotype files were not found in the 1000 Genomes annotation file, not all genes were available for testing. For the SBP phenotype, we were missing 4 causal genes, and for the DBP phenotype, we were missing 3 causal genes.To obtain a gene list to run SKAT, we used the annotation file that was constructed based on the 1000 Genomes Project  provided by GAW18 , we computed MAF based on these 142 individuals using PLINK [Y, SKAT uses a linear model i Gand covariates i Xfor subject i. As described by Wu et al [j βof an individual variant j follows an arbitrary distribution with mean 0 and variance jτw, where τ is a variance component and j wis a prespecified weight for variant j. SKAT further assumes that j; a1,a2)Beta  weighting, as shown in Figure For a continuous trait Wu et al , SKAT asle at al . WeightiBrowning . Defaultp-values for the 10,922 genes in 200 replications. To evaluate performance, we computed power (true-positive) and type I error  rates at level 0.05. For each gene, power was computed by the proportion of replicates with p < 0.05 over 200 replicates. The overall power was computed by averaging these values across all causal genes. Type I error was computed by averaging these values across all null genes.For both data sets , we ran SKAT using these 3 weighting schemes: equal weighting, Madsen-Browning weighting, and SKAT default linear weighting. For each scenario, we obtained p-value for a true positive occurred at 1.0 × 10−5 for analyses of SBP and 1.0 × 10−4 for analyses of DBP. The type I error was slightly below 0.05, roughly keeping the level 0.05. Overall power across all causal genes was about 0.05, which was almost identical to the type I error rate. The equal weighting scheme using all SNPs performed slightly better for both DBP and SBP phenotypes.Table Although overall power was similar across the 3 weighting schemes, power at each causal gene varied greatly and depended on the weighting scheme. When using all SNPs, results were considerably different across 3 weighting schemes, as shown in the left plot of Figure TXNIP, GLUL, PDCD6IP, DGKE, and SCAP, the SKAT default weighting gave much higher power than equal weighting. Except for PDCD6IP, all causal SNPs were low frequency (with MAF <0.05). Using only low-frequency SNPs provided similar performance.To understand how power depended on the weighting scheme, we selected causal genes that show power over 0.2 from at least one of weighting schemes from Figure MAP4, FLNB, DHX8, FGR, S100A6, and KRT23, the equal weighting scheme gave much higher power than the SKAT default weighting (0.32 vs. 0.04 at MAP4 for DBP). Looking within the top 20 causal variants, we found that MAP4 has 6 of the top 10 variants with the greatest effect on SBP and DBP. For all of these variants, one was common with a MAF of 0.38, and the other 5 were low frequency (with MAF <0.05). Similarly, gene FLT3 had 2 variants that fell within the top 20 causal variants, one common with a MAF of 0.42 and one rare with a MAF of 0.0016. No other causal gene had more than one variant in the top 20 causal variants. Because of this, we suspect that equal weighting performed better than SKAT default weighting, which gives a much higher weight to rarer variants.However, for genes MAP4 had the 10th lowest sig signal-to-noise ratio, and FLT3 had the 13th lowest. For the SBP phenotype, they were 7th and 10th, respectively. Therefore, we found that signal-to-noise ratio could not account for the relatively high performance on MAP4 and FLT3.We also considered whether performance would depend on the signal-to-noise ratio, defined as the number of causal variants divided by the total number of variants in the gene, for each of the top 15 genes. For DBP, SKAT has become one of the most commonly used nonburden tests for analyzing rare variants because it is fast and because nonburden tests are shown to be more powerful when most variants are noncausal or the effects of causal variants are in different directions. Using SKAT, we were able to analyze the entire sequencing data for all 200 replications of both DBP and SBP simulated phenotypes under six different cases . Performance of burden tests has been shown to depend on how to weigh rare and common variants when collapsing them in a genomic region. In this article, using the GAW 18 sequence data, we found that performance of the nonburden test SKAT also depended on the weighting scheme.MAP4 and FLT3, both of which included a common variant with a large effect.In this article, we focused on the ability of SKAT to detect genes with various MAF-based weighting schemes. When causal SNPs within a gene are mostly rare, then MAF-based weighting by upweighting rare variants and downweighting common variants would be more desirable. Indeed we found that the SKAT default weighting scheme (which heavily downweights common variants) performed better for such genes. We also found that this SKAT default weighting scheme behaved similarly to other weighting schemes after eliminating all common SNPs. In contrast, we observed that an equal weighting scheme performed the best for et al [et al [However, we found that SKAT with all 3 weighting schemes performed poorly. Overall power across all causal genes was about 0.05, which was almost identical to the type I error rate. We suspect that this poor performance is partly due to a small sample size. Wu et al  previouset al  had prevl [et al  that shoMAP4. SKAT was recently extended to handle related individuals for continuous phenotypes [Many GAW18 investigators that analyzed only unrelated individuals (with a sample size of 142) have also observed poor performance. Because of high sequencing costs, small sample sizes will continue to be a problem with rare variant analysis. We observed that analysis using all related individuals (with a sample size of 847) performed much better . For exaenotypes . We expeTo apply both burden and nonburden tests for a gene, it is necessary to know which SNPs are contained in the gene. GAW18 performed WGS of 1043 individuals of Mexican American heritage with an average 60x sequencing depth, with a goal of finding novel SNPs. However, this creates a problem for applying these rare variant approaches because available annotation information does not contain these novel variants. In particular, among 1458 causal SNPs in the Answers, only 731 SNPs were contained in the annotation file that was constructed based on the 1000 Genomes Project. In addition to a small sample size, we suspect that our results were also affected by our use of incomplete annotation information. Although the results and issues presented in this article were based on GAW18 data, they may be shared with other sequencing studies.The authors declare that they have no competing interests.CM performed the statistical analyses and drafted the manuscript. YJS designed the overall study and drafted the manuscript. Both authors read and approved the final manuscript.


This study was aimed to explore the role of long noncoding RNA C17orf91 and its potential mechanisms in ovarian cancer development.P = 0.016). Moreover, Log Rank analysis revealed that increased expression of C17orf91 was associated with shorter progression free survival(PFS)(HR = 1.90(1.19-3.03), P = 0.008). Overall survival(OS) also showed a similar trend, but did not reach statistical significance(HR = 1.75(0.97-3.13), P = 0.061). Loss-of-function studies further demonstrated that C17orf91 repression impaired migration, invasion and viability of ovarian cancer cells, and downregulated the pro-metastatic gene, MYC, at both mRNA and protein level.To assess its role in ovarian cancer, microarray datasets  in Gene Expression Omnibus (GEO) were utilized to assess the expression and clinical significance of C17orf91 in ovarian cancer. Next, loss-of-function studies were performed to establish the role of C17orf91 and the underlying mechanisms in ovarian cancer development. It was found that elevated expression of C17orf91 was observed in omental metastases when compared with matched primary ovarian tumors contains supplementary material, which is available to authorized users. Ovarian cancer is the most lethal gynecological cancer and the majority of the patients are not diagnosed until advanced stages. Over the past decades, little improvement in overall survival has been achieved despite advances in chemotherapeutic agents . Better Compelling evidence have indicated a large-scale regulatory network generated by noncoding RNAs, including microRNAs(miRNAs) and long noncoding RNAs(lncRNAs). Integrated analyses of the cancer genome and transcriptome have identified profound alterations in noncoding genes , 3. AlthIn this study, we tried to analyze C17orf91 expression and its clinical significance in ovarian cancer and to explore its role in ovarian cancer development.n = 12) and primary ovarian cancers(n = 12). GSE30587 dataset was used to determine the differential expression of C17orf91 (probe ID: 8011193) between paired primary ovarian cancer tissues(n = 9) and their corresponding omental metastases(n = 9). Relationship between C17orf91 expression (probe ID: A_23_P49610) and ovarian cancer outcome was determined using the gene expression data in 110 ovarian cancer patients(GSE17260).The publicly available microarray datasets used in our study is described in GEO database. GSE14407 dataset was used to explore the differential expression of C17orf91 (probe ID: 214696_at) between normal ovarian surface epithelium and cultured in RPMI medium 1640 plus 10 % fetal bovine serum with penicillin/streptomycin. 293 T cell line was originally obtained from the cell bank of Chinese Academy of Science (Shanghai) and cultured in DMEM plus 10 % fetal bovine serum with penicillin/streptomycin.PLKO-Scramble-shRNA was purchased from AddgeneAddgene #1864). PLKO-C17orf91-shRNA plasmids were constructed according to PLKO.1 protocol. The sequences of C17orf91 shRNA were obtained from The RNAi Consortium  and shown in Additional file 864. PLKO5 per dish and transfected with PLKO-Scramble-shRNA/PLKO-C17orf91-shRNA, as well as packing plasmids psPAX2 (0.75 μg) (Addgene#12260) and pMD2.G (0.25 μg) (Addgene #12259) using Attractene Transfection Reagent (QIAGEN). Forty-eight hours post transfection, virus-containing medium (2 ml) was harvested, filtered, mixed with 2 ml of freshsly prepared medium, supplemented with 8 μg/ml polybrene (Sigma) and added to 3 × 105 Hey cells seeded in a 3.5-cm dish the day before. Puromycin (2 μg/ml) was supplemented 48 h after infection. The cells were selected for 2 days and then used for various assays.For producing lentiviral particles, 293 T cells were seeded in 3.5-cm dishes at a density of 4 × 10TM First Strand cDNA Synthesis Kit (Fermentas). QPCR was performed using SYBR® Premix Ex Taq™ II  Kit  in ABI PRISM 7500 Sequence Detection System (Applied Biosystems). Quantitative analysis was performed using Comparative CT method. The relative expression of each gene was normalized to the expression of GAPDH. Primers used in present study were shown in Additional file Briefly, reverse transcription reactions were carried out using RevertAidWhole cell extracts were prepared in chilled RIPA lysis buffer. 30 μg of lysates protein were separated by SDS-PAGE using a 10 % polyacrylamide gel and transferred to 0.45 μm PVDF membrane(Millipore). Membranes were blocked with 5 % non-fat milk in PBS containing 0.05 % Tween-20, blotted with MYC antibody or GAPDH antibody overnight, followed by goat anti-mouse antibody. The images were scanned by LAS4000 device.4 Scramble-shRNA- and C17orf91-shRNA-transfected Hey cells were added into the RTCA CIM-Plate and monitored by xCELLigence (Roche) for 36 h.RTCA CIM-Plate was used to explore the effect of C17orf91 on migration and invasion of ovarian cancer cells. Migration and invasion assays were performed according to protocols supplied by the manufacturers. For invasion assay, the inserts were coated with BD Matrigel (B.D. Biosciences). Briefly, 4 × 103 Scramble-shRNA- and C17orf91-shRNA-transfected Hey cells were added into the RTCA E-Plate and monitored by xCELLigence (Roche) for 96 h.RTCA E-plate was used to explore the effect of C17orf91 on cell viability. Briefly, 2 × 10t test (two-tailed) was used to compare two groups. The Log-rank test was used to determine the relationship between C17orf91 expression and clinical outcomes. The Kaplan-Meier method was used to generate survival curves. P-value <0.05 was considered statistically significant.All data were analyzed with SPSS statistic software (SPSS 16.0). Student’s P = 0.078). However, elevated expression of C17orf91 was observed in omental metastases when compared with matched primary ovarian tumors.To explore the clinical significance of C17orf91 in ovarian cancer development, we first determined the differential C17orf91 expression between serous ovarian cancer tissues and normal ovarian surface epithelium(OSE) with resort to the GEO datasets. It was found that C17orf91 expression appeared to be downrelulated in ovarian cancer tissues compared with OSE, though without statistical significance. Overall survival(OS) also showed a similar trend, but did not reach statistical significance.Next, we assessed the prognostic value of C17orf91 expression by taking advantage of the microarray dataset (GSE17260) in Gene Expression Omnibus. Interestingly, Log Rank (Mantel-Cox) analysis revealed that increased expression of C17orf91 was associated with shorter progression free survival(PFS) (Fig. To explore the potential role of C17orf91 in ovarian cancer, we performed loss-of-function studies in Hey cells. Stable C17orf91-shRNA clones as well as their respective control clones were generated and examined for C17orf91 mRNA by Real-time RT-PCR Fig. . InteresTo further investigate the mechanism by which C17orf91 elicited its oncogenic role, we explored whether C17orf91 could regulate key pro-metastatic genes, such as KLF8 , MYC 1111, SNAI2C17orf91, also known as MIR22HG, was a stress responsive lncNRA . PreviouAdditionally, we characterized the clinical significances of C17orf91 in ovarian cancer tissues by taking advantages of publicly available microarray datasets in GEO database. It was noticeable that C17orf91 expression appeared to be downregulated in ovarian cancer tissues compared with OSE though without statistical significance. Indeed, such findings were not only limited in our study. For example, the well-known pro-metastatic gene miR-10b was found to be downregulated in primary breast tumors compared with normal breast tissues , 21. OurPrevious studies indicated that decreased C17orf91 expression was associated with a worse prognosis in breast and lung cancer patients , 9, 22. In conclusion, our findings revealed that C17orf91 was a potential prognostic marker and functioned as an oncogene in ovarian cancer. It remains to be seen whether modulation of C17orf91 expression will cause phenotypic changes in vivo.