m6A methylation-mediated regulation of LncRNA MEG3 suppresses ovarian cancer progression through miR-885-5p and the VASH1 pathway

Abstract Background Ovarian cancer poses a serious threat to women's health. Due to the difficulty of early detection, most patients are diagnosed with advanced-stage disease or peritoneal metastasis. We found that LncRNA MEG3 is a novel tumor suppressor, but its role in tumor occurrence and development is still unclear. Methods We investigated the expression level of MEG3 in pan-cancer through bioinformatics analysis, especially in gynecological tumors. Function assays were used to detect the effect of MEG3 on the malignant phenotype of ovarian cancer. RIP, RNA pull-down, MeRIP-qPCR, actinomycin D test were carried out to explore the m6A methylation-mediated regulation on MEG3. Luciferase reporter gene assay, PCR and Western blot were implemented to reveal the potential mechanism of MEG3. We further confirmed the influence of MEG3 on tumor growth in vivo by orthotopic xenograft models and IHC assay. Results In this study, we discovered that MEG3 was downregulated in various cancers, with the most apparent downregulation in ovarian cancer. MEG3 inhibited the proliferation, migration, and invasion of ovarian cancer cells. Overexpression of MEG3 suppressed the degradation of VASH1 by negatively regulating miR-885-5p, inhibiting the ovarian cancer malignant phenotype. Furthermore, we demonstrated that MEG3 was regulated at the posttranscriptional level. YTHDF2 facilitated MEG3 decay by recognizing METTL3‑mediated m6A modification. Compared with those injected with vector control cells, mice injected with MEG3 knockdown cells showed larger tumor volumes and faster growth rates. Conclusion We demonstrated that MEG3 is influenced by METTL3/YTHDF2 methylation and restrains ovarian cancer proliferation and metastasis by binding miR-885-5p to increase VASH1 expression. MEG3 is expected to become a therapeutic target for ovarian cancer

Effect of low-intensity pulsed ultrasound on distraction osteogenesis: a systematic review and meta-analysis of randomized controlled trials

Abstract Background Low-intensity pulsed ultrasound (LIPUS) is a common adjunct used to promote bone healing for fresh fractures and non-unions, but its efficacy for bone distraction osteogenesis remains uncertain. This study aims to determine whether LIPUS can effectively and safely reduce the associated treatment time for patients undergoing distraction osteogenesis. Methods MEDLINE, EMBASE, and the Cochrane Library were searched until May 1, 2018, without language restriction. Studies should be randomized controlled trials (RCTs) or quasi-RCTs of LIPUS compared with sham devices or no devices in patients who undergo distraction osteogenesis. The primary outcome was the treatment time. The secondary outcome was the risk of complications. Treatment effects were assessed using mean differences, standardized mean differences, or risk ratios using a random-effects model. The Cochrane risk-of-bias tool was used to assess the risk of bias. The I 2 statistic was used to assess the heterogeneity. The GRADE system was used to evaluate the evidence quality. Results A total of 7 trials with 172 patients were included. The pooled results suggested that during the process of distraction osteogenesis, LIPUS therapy did not show a statistically significant reduction in the treatment time (mean difference, − 8.75 days/cm; 95% CI, − 20.68 to 3.18 days/cm; P = 0.15; I 2 = 72%) or in the risk of complications (risk ratio, 0.90 in favor of LIPUS; 95% CI, 0.65 to 1.24; I 2 = 0%). Also, LIPUS therapy did not show a significant effect on the radiological gap fill area (standardized mean difference, 0.48 in favor of control; 95%CI, − 1.49 to 0.52; I 2 = 0%), the histological gap fill length (standardized mean difference, 0.76 in favor of control; 95%CI, − 1.78 to 0.27; I 2 = 0%), or the bone density increase (standardized mean difference, 0.43 in favor of LIPUS; 95%CI, − 0.02 to 0.88; I 2 = 0%). Conclusions Among patients undergoing distraction osteogenesis, neither the treatment time nor the risk of complications could be reduced by LIPUS therapy. The currently available evidence is insufficient to support the routine use of this intervention in clinical practice. Trial registration CRD 4201707359

Mutations in the p53 tumor suppressor gene and early onset breast cancer

Among breast cancer patients p53 gene mutation is associated with a poor prognosis. Young women with breast cancer are more likely than older women to have a poor prognosis, but whether p53 gene mutation plays a role in breast cancer in young women is not clear. This study identified 199 breast cancer patients and tested the hypothesis that p53 gene mutation was associated with early onset breast cancer. Patients with p53 gene mutations were 3-times more likely to have an early onset breast cancer (age < or = 40 years at diagnosis) than those without p53 mutations (OR = 3.05, 95% CI = 1.10-8.45). Patients with both missense and silent mutations were 7-times more likely to have a diagnosis of early onset breast cancer (OR = 7.56, 95% CI = 2.22-25.8). Patients with mutations in exon 8 of the p53 gene were 6-times more likely to be diagnosed with early onset breast cancer (OR = 6.48, 95% CI = 1.37-30.6). These findings suggest that p53 gene mutation may hasten the onset of female breast cancer

The Effect of Teriparatide on Fracture Healing of Osteoporotic Patients: A Meta-Analysis of Randomized Controlled Trials

. Purpose. This meta-analysis is to assess the effectiveness of teriparatide in fracture healing and clinical function improvement of the osteoporotic patients. Methods. We searched PubMed, Embase, Web of Science, and the Cochrane databases for randomized and quasi-randomized controlled trials comparing teriparatide to placebo, no treatment, or comparator interventions in the osteoporotic patients

Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trialsResearch in context

Summary: Background: The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy. Methods: Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658). Findings: This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294–0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390–0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158–1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159–1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270–1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277–1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144–0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144–0.259; I2 = 68.15%). Interpretation: AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence. Funding: This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China’s General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023)

The Ratio of the Hemoglobin to Red Cell Distribution Width Combined with the Ratio of Platelets to Lymphocytes Can Predict the Survival of Patients with Gastric Cancer Liver Metastasis

Background. Hemoglobin/red cell distribution width (HR) and platelet/lymphocyte (PLR) ratios are considered effective prognostic markers in various cancers. We have proposed a new prognostic parameter: HR+PLR. The aim of this study is to explore the prognostic value of the HR+PLR scoring system in patients with gastric cancer liver metastasis. Methods. This study retrospectively analyzed the clinical data of 306 patients with gastric cancer liver metastases admitted to our hospital from 2007 to 2014. According to the size of HR value and PLR value, we will divide the patients into three groups, namely, HR+PLR: (1) 0 points: HR>1.02 and PLR1.02 and PLR>128 and HR128. Results. The HR+PLR score was statistically different from age (P=0.049), T stage (P<0.001), N stage (P=0.017), number of liver metastases (P=0.018), gastrectomy (P<0.001), hepatectomy (P=0.001), peritoneal metastasis (P=0.012), prognostic nutritional index (PNI) (P=0.028), and neutrophil/lymphocyte ratio (NLR) (P=0.045). The HR+PLR scoring system has a higher area under the ROC curve (AUC value) than PNI, PLR, HR, and PLR (AUC=0.798, P<0.001). In multivariate analysis, gastrectomy (P=0.001), hepatectomy (P<0.001), chemotherapy (P=0.014), and HR+PLR score (P<0.001) were considered independent prognostic factors. Conclusion. For patients with gastric cancer liver metastasis, the HR+PLR score is a simple, reliable, and economic prognostic marker

Prognostic importance of the preoperative New‐Naples prognostic score for patients with gastric cancer

Abstract Background The wide applicability of the Naples prognostic score (NPS) is still worthy of further study in gastric cancer (GC). This study aimed to construct a New‐NPS based on the differences in immunity and nutrition in patients with upper and lower gastrointestinal tumors to help obtain an individualized prediction of prognosis. Methods This study retrospectively analyzed patients who underwent radical gastrectomy from April 2014 to September 2016. The cutoff values of the preoperative neutrophil‐to‐lymphocyte ratio (NLR), lymphocyte‐to‐monocyte ratio (LMR), serum albumin (Alb), and total cholesterol (TC) were calculated by ROC curve analysis. ROC and t‐ROC were used to evaluate the accuracy of the prognostic markers. The Kaplan–Meier method and log‐rank test were used to analyze the overall survival probability. Univariate and multivariate analyses based on Cox risk regression were used to show the independent predictors. The nomogram was made by R studio. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C‐index), and decision curve. Results A total of 737 patients were included in training cohort, 411 patients were included in validation cohort. ROC showed that the New‐NPS was more suitable for predicting the prognosis of GC patients. NPS = 2 indicated a poor prognosis. Multivariate analysis showed that CEA (P = 0.026), Borrmann type (P = 0.001), pTNM (P < 0.001), New‐NPS (P < 0.001), and nerve infiltration (P = 0.035) were independent risk factors for prognosis. Conclusion The New‐NPS based on the cutoff values of NLR, LMR, Alb, and TC is not only suitable for predicting prognosis but can also be combined with clinicopathological characteristics to construct a nomogram model for GC patients

Role of corin in trophoblast invasion and uterine spiral artery remodelling in pregnancy

In pregnancy, trophoblast invasion and uterine spiral artery remodelling are important for lowering maternal vascular resistance and increasing uteroplacental blood flow. Impaired spiral artery remodelling has been implicated in pre-eclampsia, a major complication of pregnancy, for a long time but the underlying mechanisms remain unclear1, 2. Corin (also known as atrial natriuretic peptide-converting enzyme) is a cardiac protease that activates atrial natriuretic peptide (ANP), a cardiac hormone that is important in regulating blood pressure3. Unexpectedly, corin expression was detected in the pregnant uterus4. Here we identify a new function of corin and ANP in promoting trophoblast invasion and spiral artery remodelling. We show that pregnant corin- or ANP-deficient mice developed high blood pressure and proteinuria, characteristics of pre-eclampsia. In these mice, trophoblast invasion and uterine spiral artery remodelling were markedly impaired. Consistent with this, the ANP potently stimulated human trophoblasts in invading Matrigels. In patients with pre-eclampsia, uterine Corin messenger RNA and protein levels were significantly lower than that in normal pregnancies. Moreover, we have identified Corin gene mutations in pre-eclamptic patients, which decreased corin activity in processing pro-ANP. These results indicate that corin and ANP are essential for physiological changes at the maternal–fetal interface, suggesting that defects in corin and ANP function may contribute to pre-eclampsia

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Abstract Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC