[RETRACTED ARTICLE]: Expression of serum microRNA-378 and its clinical significance in renal cell carcinoma
Authors: Lixin Shi
Xiyuan Cao
Lei Zhang
Shengkun Sun
Chunyang Wang
Xu Zhang
Journal: Genetics and Molecular Biology (2017)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: miRNA-378; renal cell carcinoma; serum; clinical significance; expression
Affiliations: Department of Urology, PLA General Hospital, Beijing, China., Institute of Basic Medicine, Military Medical Science Academy of the PLA, Beijing, China.

Abstract:  Studies have demonstrated that miRNA-378 is expressed in various malignant tumors. In the present study, we aimed to explore the expression of serum miRNA-378 and its clinical significance in renal cell carcinoma (RCC) patients. A total of 75 RCC patients, 63 renal cysts (RC) patients and 75 healthy controls were selected. The miRNA-378 level in RCC and RC groups was significantly higher than in healthy control group, with RCC group having the highest level. The miRNA-378 levels were significantly decreased within the same group after surgery. When compared with healthy controls, RC group had higher levels but not significantly (p &gt; 0.05) while levels in RCC group were significantly higher (p &lt; 0.05). miRNA-378 expression was correlated with clinical stage and differentiation degree, but not correlated with patient's age, gender, surgical strategy and tumor diameter. The AUC of miRNA-378 was 0.896, 95% confidence interval was 0.847 to 0.945, and AUC hypothesis testing was statistically significant (p &lt; 0.001, RCC vs healthy control). miRNA-378 shows potential in the diagnosis and prediction of postoperative curative effect of renal cell carcinoma, but further studies with lager samples are needed. 

Retracted: Yes

PubPeer comments:
 I wanted to highlight the effect sizes reported in this paper. For context, the authors are testing the impact of temperature change on auction behaviors in computerized lab experiments, and predicting a mediation by feelings of hostile aggression. They find effects that are as large as d = 2.14, and often larger than d = 1.5. Some useful benchmarks to evaluate the Cohen s d that are about to follow. The following effect sizes were obtained from a sample of 697 participants on MTurk (see http: datacolada.org 18 ): * Men are taller than women: d = 1.83 * Men are heavier than women: d = .57 * Liberals like Michelle Obama more than conservatives: d = .85 * People who like spicy food like Indian food more than those who do not like spicy food: d = .79. All Cohenu2019s d reported below are calculated from the F and Chi-Square statistics reported in the paper. When per-cell sample size was not available, I assumed that that participants were equally distributed across cells. * Study 1: Two cells design. Temperature manipulated to be low (67u00b0F = 19.4u00b0C) vs. high (77u00b0F = 25u00b0C) * For reference, manipulation check (how hot participants felt). Cohenu2019s d = 1.73 * Temperature on pre-auction valuation. Cohenu2019s d = 0.74 * Temperature on WTP. Cohenu2019s d = 1.54 * Temperature on number of offers. Cohenu2019s d = 1.55 * Study 2A: Two by three design. Temperature manipulated as before, plus selling mechanism (auction vs. negotiation vs. fixed price). * Degrees of freedom appear to be incorrect. They mention 156 participants, but often report tests with 106 degrees of freedom. * For reference, manipulation check (how hot participants felt). Cohenu2019s d = 0.71 * Main effect of temperature. Cohenu2019s d = .66 * Conditional effect of temperature among bidders. Cohenu2019s d = 2.14. * Main effect of temperature on state aggression. Cohenu2019s d = .82 * Study 2B: Multiple levels of temperature. Effect sizes cannot be calculated from the information provided (only omnibus F-stats, or cell means without cells SD, are reported). * Study 2C: Two by two. Two levels of temperature manipulated to be low (67u00b0F = 19.4u00b0C) vs. very high (82u00b0F = 27.8u00b0C), plus selling mechanism (auction vs. negotiation). * For reference, manipulation check (how hot participants felt). Cohenu2019s d = 2 * Conditional effect of temperature on WTP among bidders. Cohenu2019s d = 2.05. As large as the manipulation check. * Conditional effect of temperature on WTP among negotiators. Cohenu2019s d = 1.01 * The correlation between comfort and hostile aggression appears very large, around .4 (see table 3 in the paper, model 2). * Study 3: Two by two. Temperature manipulated as in study 1, plus role (seller vs. buyer). * Degrees of freedom are also inconsistent across tests. * For reference, manipulation check (how hot participants felt). Cohenu2019s d = .48 * Main effect of temperature on agreement likelihood. Cohenu2019s d = 0.76 * Main effect of temperature on number of offers. Cohenu2019s d = 0.89 * Main effect of temperature on number on concessions. Cohenu2019s d = 0.62 * Main effect of temperature on final agreement price. Cohenu2019s d = 1.39. Almost three times as large as the effect size of the manipulation check. * Study 4. Two by two. Temperature manipulated as in study 1, plus competitiveness (yes or control). * Effect sizes are smaller in this study (but still large). * For reference, manipulation check (how hot participants felt). Cohenu2019s d = .74 * Conditional effect of temperature among competitive on time spent. Cohenu2019s d = .6 * Conditional effect of temperature among control on time spent. Cohenu2019s d= .15. , 
 A few afterthoughts thanks to a careful reader. First, the degrees of freedom are not inconsistent. Some analysis were performed on subsets of the data, and reported as such. Second, there is another helpful reference to evaluate the size of those effects: A recent meta-analysis of effect sizes in marketing (Eisend 2015), combining 1841 meta-analytical effect sizes of more than 7500 marketing studies. The maximum effect size observed was .96. In comparison, the observed impact on WTP in a bidding context was between 1.54 (study 1) and 2.14 (study 21). Reference: Eisend, M. (2015). Have we progressed marketing knowledge? A meta-meta-analysis of effect sizes in marketing research. Journal of Marketing, 79(3), 23-40. , 
 Could Dr. Sinha and Dr. Bagchi confirm that those values are indeed accurate, and perhaps share more information about the raw data? A histogram of the WTP by condition for instance? , 
 I can confirm that several of the effect sizes are quite large. I would further add that effect sizes in Study 2b can be calculated using Table 2. Study 2b, 82F vs 70F: Auction: - WTP: d = 1.36 - Purchase likelihood: d = 0.84 Negotiation - WTP: d = 1.25 - Purchase likelihood: d = 2.06 An effect of two standard deviations would surely be noticed by now and Sotheby s would hold auctions in an optimally-uncomfortable 82 degrees. Since this is undergraduate data, not proprietary corporate data, I think it would be helpful if the authors would post the raw data for confirmation. , 
 JM just posted an expression of concern: https: journals.sagepub.com doi full 10.1177 00222429211026311 ![file] , 
 Copy pasted from https: journals.sagepub.com doi 10.1177 00222429211026311 :  The American Marketing Association (AMA) issues an expression of concern related to Jayati Sinha and Rajesh Bagchi (2019), u201cRole of Ambient Temperature in Influencing Willingness to Pay in Auctions and Negotiations,u201d Journal of Marketing, 83 (4), 121u2013138 at the suggestion of Editor in Chief Christine Moorman. A third party contacted the journal with concerns that the data in this article may have been fabricated, based on the identification of unusual patterns in three studies reported in the article. After receipt of the concerns, the journal arranged for a review by a panel of independent scholars. The authors fully cooperated with the independent review by providing copies of the original completed questionnaires and associated spreadsheet files used in the analysis for studies 2a, 2b, and 2c u2013 the three studies under question. The independent reviewers were unable to confirm the third-partyu2019s concerns about data fabrication. However, in comparing the original questionnaires and Excel files, four anomalies were identified: (1) data appearing in the spreadsheet files when no values were present in the original surveys; (2) data in the spreadsheet files having different values from those that appear in the original surveys; (3) large outliers (outside the valid response range) whose values were changed in the entered data; and (4) entries in the original survey being scratched over with new results circled. Anomalies were found in all three studies, with Study 2b having the largest number. The anomalies described in points (2) and (4) were deemed less serious as the anomalies described in point (2) could be attributed to random data entry errors, and the changed entries described in point (4) may happen in surveys and experiments. Point (1), the entry of data in the spreadsheet files that was not present in the raw files, and point (3), the failure to identify the occurrence of outliers and the action of changing the outlier data to results more consistent with other reported data without disclosing these changes in the article, were deemed more serious infractions of data management. The number of instances of (1) and (3) are very small relative to the number of data points checked in Studies 2au20132c. Upon independently reanalyzing the data, it was found that the results do not change when correcting for these anomalies. However, given the nature of the errors, an Expression of Concern was deemed prudent. The first author, Jayati Sinha, had informed the journal that the data were entered exclusively by the first author. First Published August 20, 2021. See also https: retractionwatch.com 2021 08 23 readers-puzzle-over-marketing-journals-failures-to-retract ,