Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers.

Due to the heterogeneity of cancer, precision medicine has been a major challenge for cancer treatment. Determining medication regimens based on patient genotypes has become a research hotspot in cancer genomics. In this study, we aim to identify key biomarkers for targeted therapies based on single nucleotide variants (SNVs) and copy number variants (CNVs) of genes. The experiment is carried out on 7 cancers on the Encyclopedia of Cancer Cell Lines (CCLE) dataset. Considering the high mutability of driver genes which result in abundant mutated samples, the effect of data sparsity can be eliminated to a large extent. Therefore, we focus on discovering the relationship between driver mutation patterns and three measures of drug response, namely area under the curve (AUC), half maximal effective concentration (EC50), and log2-fold change (LFC). First, multiple statistical methods are applied to assess the significance of difference in drug response between sample groups. Next, for each driver gene, we analyze the extent to which its mutations can affect drug response. Based on the results of multiple hypothesis tests and correlation analyses, our main findings include the validation of several known drug response biomarkers such as BRAF, NRAS, MAP2K1, MAP2K2, and CDKN2A, as well as genes with huge potential to infer drug responses. It is worth emphasizing that we identify a list of genes including SALL4, B2M, BAP1, CCDC6, ERBB4, FOXA1, GRIN2A, and PTPRT, whose impact on drug response spans multiple cancers and should be prioritized as key biomarkers for targeted therapies. Furthermore, based on the statistical p-values and correlation coefficients, we construct gene-drug sensitivity maps for cancer drug recommendation. In this work, we show that driver mutation patterns could be used to tailor therapeutics for precision medicine.

Comparative secretome analysis reveals cross-talk between type III secretion system and flagella assembly in Pseudomonas plecoglossicida.

The Gram-negative bacterium Pseudomonas plecoglossicida has caused visceral granulomas disease in several farmed fish species, including large yellow croaker (Larimichthys crocea), which results in severe economic losses. Type III secretion systems (T3SS) are protein secretion and translocation nanomachines widely employed by many Gram-negative bacterial pathogens for infection and pathogenicity. However, the exact role of T3SS in the pathogenesis of P. plecoglossicida infection is still unclear. In this study, a T3SS translocators deletion strain (△popBD) of P. plecoglossicida was constructed to investigate the function of T3SS. Then comparative secretome analysis of the P. plecoglossicida wild-type (WT) and △popBD mutant strains was conducted by label-free quantitation (LFQ) mass spectrometry. The results show that knockout of T3SS translocators popB and popD has an adverse effect on the effector protein ExoU secretion, flagella assembly, and biofilm formation. Further experimental validations also confirmed that popB-popD deletion could affect the P. plecoglossicida flagella morphology/formation, adherence, mobility, and biofilm formation. These data indicate that a cross-talk exists between the P. plecoglossicida T3SS and the flagella system. Our results, therefore, will facilitate the further under-standing of the pathogenic mechanisms leading to visceral granulomas disease caused by P. plecoglossicida.

A diagnostic scoring model of ENKTCL in the nose-Waldeyer's ring based on logistic regression: Differential diagnosis from DLBCL.

Objective: To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr (EB) virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type (ENKTCL) from diffuse large B cell lymphoma (DLBCL). Methods: This study population was obtained from two independent hospitals. A total of 89 patients with ENKTCL (n = 36) or DLBCL (n = 53) from January 2013 to May 2021 were analyzed retrospectively as the training cohort, and 61 patients (ENKTCL=27; DLBCL=34) from Jun 2021 to Dec 2022 were enrolled as the validation cohort. All patients underwent CT/MR enhanced examination and EB virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features and EB virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model. Results: We searched for significant clinical characteristics, imaging characteristics and EB virus nucleic acid and constructed the scoring system via multivariate logistic regression and converted regression coefficients to weighted scores. The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including site of disease (nose), edge of lesion (blurred), T2WI (high signal), gyrus like changes, EB virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4 points. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. The AUC of the scoring model in the training cohort were 0.925 (95% CI, 0.906-0.990) and the cutoff point was 5 points. In the validation cohort, the AUC was 0.959 (95% CI, 0.915-1.000) and the cutoff value was 6 points. Four score ranges were as follows: 0-6 points for very low probability of ENKTCL, 7-9 points for low probability; 10-11 points for middle probability; 12-16 points for very high probability. Conclusion: The diagnostic score model of ENKTCL based on Logistic regression model which combined with imaging features and EB virus nucleic acid. The scoring system was convenient, practical and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.

Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.

In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an R 2 and RMSE of 0.87 and 0.53, respectively. Through data validation, the significance of assessing the reliability of predictions by predicting confidence intervals and its role in personalized medicine are illustrated. The correlation analysis of the genes selected from the three layers of features also shows the effectiveness of our proposed methods.

Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods.

Purpose: To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods. Methods: A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion - post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram's performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis. Results: The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002-1.024; p=0.018), LNS changes (pre-NAT LNS - post-NAT LNS; OR, 2.790; 95% CI, 1.190-6.544; p=0.018), N stage (OR, 0.496; 95% CI, 0.269-0.915; p=0.025), and HER2 status (OR, 2.244; 95% CI, 1.147-4.392; p=0.018) were independent predictors of apCR. The AUCs of the nomogram were 0.74 (95% CI, 0.68-0.81) and 0.76 (95% CI, 0.63-0.90) for training and validation sets, respectively. In RF model, the maximum diameter of the primary lesion, axillary lymph node, and LNS in each cycle, estrogen receptor status, progesterone receptor status, HER2, Ki67, and T and N stages were included in the training set. The final validation set had an AUC value of 0.85 (95% CI, 0.74-0.87). Conclusion: Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient's clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.

Zn2+ Cross-Linked Alginate Carrying Hollow Silica Nanoparticles Loaded with RL-QN15 Peptides Provides Promising Treatment for Chronic Skin Wounds.

Chronic and non-healing wounds pose a great challenge to clinical management and patients. Many studies have explored novel interventions against skin wounds, with bioactive peptides, nanoparticles, and hydrogels arousing considerable attention regarding their therapeutic potential. In this study, the prohealing peptide RL-QN15 was loaded into hollow silica nanoparticles (HSNs), with these HSN@RL-QN15 nanocomposites then combined with zinc alginate (ZA) gels to obtain HSN@RL-QN15/ZA hydrogel. The characteristics, biological properties, and safety profiles of the hydrogel composites were then evaluated. Results showed that the hydrogel had good porosity, hemocompatibility, biocompatibility, and broad-spectrum antimicrobial activity, with the slow release of loaded RL-QN15. Further analysis indicated that the hydrogel promoted skin cell proliferation and keratinocyte scratch repair, regulated angiogenesis, reduced inflammation, and accelerated re-epithelialization and granulation tissue formation, resulting in the rapid healing of both full-thickness skin wounds and methicillin-resistant Staphylococcus aureus biofilm-infected chronic wounds in mice. This peptide-based hydrogel provides a novel intervention for the treatment of chronic skin wounds and shows promise as a wound dressing in the field of tissue regeneration.

BUSnet: A Deep Learning Model of Breast Tumor Lesion Detection for Ultrasound Images.

Ultrasound (US) imaging is a main modality for breast disease screening. Automatically detecting the lesions in US images is essential for developing the artificial-intelligence-based diagnostic support technologies. However, the intrinsic characteristics of ultrasound imaging, like speckle noise and acoustic shadow, always degenerate the detection accuracy. In this study, we developed a deep learning model called BUSnet to detect the breast tumor lesions in US images with high accuracy. We first developed a two-stage method including the unsupervised region proposal and bounding-box regression algorithms. Then, we proposed a post-processing method to enhance the detecting accuracy further. The proposed method was used to a benchmark dataset, which includes 487 benign samples and 210 malignant samples. The results proved the effectiveness and accuracy of the proposed method.

Factors Influencing Total Delay of Breast Cancer in Northeast of China.

Objectives: Delay in diagnosis and treatment, called total delay, could probably result in lower survival rates in breast cancer patients. This study aimed to investigate the factors associated with the comprehensive delay behaviors and to evaluate its effect on outcomes in patients with breast cancer in Dalian, a northeast city of China. Methods: A retrospective chart review was conducted using a cancer registry dataset including 298 patients. The Kaplan-Meier survival analysis was used to identify the threshold of total delay, dividing the patients into a group with significant uncertainty and a group without substantial delay. The factors associated with the significant total delay were investigated from the potential candidates, like income level and marital status, by using the chi-squared test. The difference of the clinicopathologic characteristics between the patients grouped by the significant total delay, like tumor size and lymph node metastasis, was also investigated to find out the effect of the total delay. Results: A total of 238 charts were used for analysis. The mean age was 57.3. The median of total delays was 3.75 months. Thirty days was identified as a threshold, more than which the total delay can lead to worse survival. Patients' marital status (p = 0.010), income levels (p = 0.003), smoking status (p = 0.031), initial visiting hospital level (p = 0.005), self-health care (p = 0.001), and self-concern about initial symptom (p ≈ 0.000) were identified as the independent predictors of the total delay. Metastasis (p ≈ 0.000) was identified as the significant result relating to the significant total delay. Conclusions: A total delay of more than 30 days predicts worse survival in breast cancer patients in Dalian. Several factors, like patients' marital status and income levels, can be considered to be relevant to the significant total delay. We recommend that these factors be used to predict the potential patients with the significant total delay in the clinical practice.

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers.

Objective: To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively. Methods: A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Results: Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort. Conclusion: We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.

Mesoporous polydopamine nanoparticles carrying peptide RL-QN15 show potential for skin wound therapy.

BACKGROUND: Skin wound healing remains a considerable clinical challenge, thus stressing the urgent need for the development of new interventions to promote repair. Recent researches indicate that both peptides and nanoparticles may be potential therapies for the treatment of skin wounds. METHODS: In the current study, the mesoporous polydopamine (MPDA) nanoparticles were prepared and the peptide RL-QN15 that was previously identified from amphibian skin secretions and exhibited significant potential as a novel prohealing agent was successfully loaded onto the MPDA nanoparticles, which was confirmed by results of analysis of scanning electron microscopy and fourier transform infrared spectroscopy. The encapsulation efficiency and sustained release rate of RL-QN15 from the nanocomposites were determined. The prohealing potency of nanocomposites were evaluated by full-thickness injured wounds in both mice and swine and burn wounds in mice. RESULTS: Our results indicated that, compared with RL-QN15 alone, the prohealing potency of nanocomposites of MPDA and RL-QN15 in the full-thickness injured wounds and burn wounds in mice was increased by up to 50 times through the slow release of RL-QN15. Moreover, the load on the MPDA obviously increased the prohealing activities of RL-QN15 in full-thickness injured wounds in swine. In addition, the obvious increase in the prohealing potency of nanocomposites of MPDA and RL-QN15 was also proved by the results from histological analysis. CONCLUSIONS: Based on our knowledge, this is the first research to report that the load of MPDA nanoparticles could significantly increase the prohealing potency of peptide and hence highlighted the promising potential of MPDA nanoparticles-carrying peptide RL-QN15 for skin wound therapy.

Corrigendum: FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.

[This corrects the article DOI: 10.3389/fgene.2020.564839.].

Occurrence, spatial distribution, source, and ecological risk assessment of organochlorine pesticides in Dongting Lake, China.

The occurrence, distribution, sources, and ecological risks of organochlorine pesticides in Dongting Lake of China were investigated. The average concentrations of organochlorine pesticides (OCPs) in 22 surface water samples and 14 sediment samples were 90.07 ng/L and 80.65 ng/g dw, respectively. Sixteen types of OCPs, dominated by HCHs, DDTs and heptachlor, were detected in the Dongting Lake. The relationships of OCP residues between Dongting Lake and its tributary rivers have been discussed and the hydraulic connections with the Yangtze River and the Three Gorges Dam (TGD) were also considered. Results showed that the shortage of runoff, earlier dry season, and reduction of sediment deposition extremely deteriorated the hydraulic conditions, magnified the water cycle, and restrained the self-purification of OCPs. The ∑OCPs in surface water were concentrated in the inlets of Yangtze River, Lishui River, Zishui River, Yuanshui River, and Xiangjiang River. Moreover, the ∑OCPs in the outlet of the Yangtze River also maintained a high level, indicating that OCPs posed adverse effects on the Yangtze River. Risk assessments of OCPs in the surface water of Dongting Lake were estimated according to available water quality guidelines and health risk assessment models. The results indicated that OCPs in the surface water of Dongting Lake were safe for aquatic organisms and human health. In addition, sediment quality guidelines (SQGs) were also applied to evaluate the potential ecotoxicological risks of OCPs in sediments. The results presented that contaminants of γ-HCH; o,p'-DDD; and dieldrin in sediment had adverse effects on benthic organisms, indicating that fundamental solutions should be proposed to control OCP contamination in Dongting Lake.

Occurrence and risk evaluation of organophosphorus pesticides in typical water bodies of Beijing, China.

AbstractHuman activities, particularly in large cities, can lead to pollution caused by micropollutants such as pesticides in water bodies, which have been recognized as serious threats to the environment and human health. The pollution level of six organophosphorus pesticides, three herbicides, and one bactericide in groundwater and the Wenyu River, and their fates in three sewage treatment plants (STPs) and a hospital were investigated in this study. The concentrations of the ten detected pesticides ranged from not detected (ND) to 323.44 ng L-1 in different water samples from Beijing; metalaxyl was detected to have the highest concentration (89.58 ng L-1), and the detection frequencies of atrazine and metalaxyl were 100%. The maximum concentrations of pesticides in the Wenyu River, STPs, and the hospital were 1-2 orders of magnitude higher than those in the groundwater. Good removal efficiencies by the treatment processes were observed for ametryn (100%), while the removal efficiencies for atrazine and omethoate were the lowest in the three STPs (- 9.6% and 12.67%, respectively). Finally, risk quotient (RQ) values of each contaminant were estimated from the maximum values determined for typical urban to assess the ecology and health effects. In the case of environmental toxicity, the highest RQ values (> 1) were obtained for dichlorvos and omethoate. In the case of health toxicity, the RQ values show that the pesticides found in groundwater pose no potential health risks to humans at current concentrations.

A scoring model for radiologic diagnosis of gastric leiomyomas (GLMs) with contrast-enhanced computed tomography (CE-CT): Differential diagnosis from gastrointestinal stromal tumors (GISTs).

OBJECTIVES: To investigate CT findings and develop a diagnostic score model to differentiate GLMs from GISTs. METHODS: This retrospective study included 109 patients with pathologically confirmed GLMs (n = 46) and GISTs (n = 63) from January 2013 to August 2018 who received CE-CT before surgery. Demographic and radiological features was collected, including lesion location, contour, presence or absence of intralesional necrosis and ulceration, growth pattern, whether the tumor involved EGJ, the long diameter (LD) /the short diameter (SD) ratio, pattern and degree of lesion enhancement. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors and establish a predictive model. Independent predictors for GLMs were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the model. Overall score distribution was divided into four groups to show differentiating probability of GLMs from GISTs. RESULTS: Five CT features were the independent predictors for GLMs diagnosis in multivariate logistic regression analysis, including esophagogastric junction (EGJ) involvement (OR, 367.9; 95 % CI, 5.8-23302.8; P =  0.005), absence of necrosis (OR, 11.9; 95 % CI, 1.0-138. 1; P =  0.048) and ulceration (OR, 151.9; 95 % CI, 1.4-16899.6; P =  0.037), degree of enhancement (OR, 9.3; 95 % CI, 3.2-27.4; P <  0.001), and long diameter/ short diameter (LD/SD) ratio (OR,170.9; 95 % CI, 8.4-3493.4; P =  0.001). At a cutoff of 9 points, AUC for this score model was 0.95, with 95.65 % sensitivity, 79.37 % specificity, 77.19 % PPV, 96.15 % NPV and 86.24 % diagnostic accuracy. An increasing trend was showed in diagnostic probability of GLMs among four groups based on the score (P <  0.001). CONCLUSIONS: The newly designed scoring system is reliable and easy-to-use for GLMs diagnosis by distinguishing from GISTs, including EGJ involvement, absence of ulceration and necrosis, mild enhancement and high LD/SD ratio. The overall score of model ranged from 1 to 17 points, which was divided into 4 groups: 1-7 points, 7-10 points, 10-13 points and 13-17 points, with a diagnostic probability of GLMs 0%, 45 %, 83 % and 100 %, respectively.

Discovery of a novel short peptide with efficacy in accelerating the healing of skin wounds.

Despite extensive efforts to develop efficacious therapeutic approaches, the treatment of skin wounds remains a considerable clinical challenge. Existing remedies cannot sufficiently meet current needs, so the discovery of novel pro-healing agents is of growing importance. In the current research, we identified a novel short peptide (named RL-QN15, primary sequence 'QNSYADLWCQFHYMC') from Rana limnocharis skin secretions, which accelerated wound healing in mice. Exploration of the underlying mechanisms showed that RL-QN15 activated the MAPK and Smad signaling pathways, and selectively modulated the secretion of cytokines from macrophages. This resulted in the proliferation and migration of skin cells and dynamic regulation of TGF-ß1 and TGF-ß3 in wounds, which accelerated re-epithelialization and granulation tissue formation and thus skin regeneration. Moreover, RL-QN15 showed significant therapeutic potency against chronic wounds, skin fibrosis, and oral ulcers. Our results highlight frog skin secretions as a potential treasure trove of bioactive peptides with healing activity. The novel peptide (RL-QN15) identified in this research shows considerable capacity as a candidate for the development of novel pro-healing agents.

FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.

Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.

CircRNA TADA2A relieves idiopathic pulmonary fibrosis by inhibiting proliferation and activation of fibroblasts.

The excessive activation and proliferation of lung fibroblasts are responsible for the abundant deposition of extracellular matrix (ECM) in idiopathic pulmonary fibrosis (IPF), while its specific mechanism is still unknown. This study focuses on the role of circRNA (circ) TADA2A in functional abnormalities of lung fibroblasts and aims to elaborate its regulatory mechanism. In the present study, circTADA2A was downregulated in both IPF primary human lung fibroblasts and human IPF fibroblastic cell lines. Functionally, the overexpression of circTADA2A repressed the activation and proliferation of normal human fibroblastic cell line induced by several fibrogenic growth factors. Using fluorescence in situ hybridization (FISH), luciferase reporter assays, and RNA pull-down, circTADA2A was confirmed to function as sponges of miR-526b and miR-203, thus releasing the expression of Caveolin (Cav)-1 and Cav2. The overexpression of circTADA2A suppressed lung-fibroblasts activation via Cav1 and reduced lung-fibroblasts proliferation via Cav2. In vivo experiments also confirmed that the overexpression of circTADA2A decreased fibrogenic responses induced by bleomycin in lung-fibrosis mice. Collectively, circTADA2A repressed lung-fibroblasts activation via miR-526b/Cav1 and reduced lung-fibroblasts proliferation via miR-203/Cav2, thus inhibiting the excessive deposition of ECM and relieving IPF.

Long non-coding RNA PVT1 encapsulated in bone marrow mesenchymal stem cell-derived exosomes promotes osteosarcoma growth and metastasis by stabilizing ERG and sponging miR-183-5p.

Exosomes secreted by bone marrow mesenchymal stem cells (BMSCs) promote osteosarcoma cell proliferation and migration, while the underlying mechanism remains unknown. Since the long non-coding RNA PVT1 has been reported to be upregulated in osteosarcoma cells and contributes to its growth and metastasis, we aim to investigate whether BMSC-derived exosomes promote osteosarcoma growth and metastasis via transporting PVT1 into osteosarcoma cells. The PVT1 expression in BMSC-derived exosomes was markedly higher than that in osteosarcoma cell-derived exosomes. The co-culturing of BMSC-derived exosomes and osteosarcoma cells (Saos-2, MG-63, and MNNG/HOS cell lines) significantly raised PVT1 expression of osteosarcoma cells. The direct binding between PVT1 and the oncogenic protein ERG was confirmed using RNA immunoprecipitation and RNA pull-down assays, and the transported PVT1 promotes osteosarcoma cell proliferation and migration via inhibiting degradation and ubiquitination of ERG. PVT1 also increased ERG expression through sponging miR-183-5p. In summary, our findings indicated that BMSC-derived exosomes encapsulate PVTl and transport it into osteosarcoma cells, and the transported PVT1 promotes tumor growth and metastasis by inhibiting ubiquitination and promoting expression of ERG in osteosarcoma cells. These data provide a novel insight into the mechanism of BMSC-derived exosomes in affecting osteosarcoma progression.

Wilms tumor-suppressing peptide inhibits proliferation and induces apoptosis of Wilms tumor cells in vitro and in vivo.

BACKGROUND: Our previous study identified a Wilms tumor-suppressing peptide (WTSP) that was upregulated in healthy children, but downregulated in children with Wilms tumor (WT). This study aimed to investigate the effect of WTSP on WT growth in vivo and in vitro. METHODS: WTSP was synthesized by solid-phase synthesis of FOMC-protected amino acids. Cell growth curve, cytotoxicity, and apoptosis of WTSP-treated human WT cell line (SK-NEP-1) were determined by cell count, Cell Counting Kit-8 assay, and flow cytometry. The expression of key proteins of four WT-associated signaling pathways was determined by real-time PCR and western blotting. The WT xenograft mouse model was established by the armpit injection of SK-NEP-1 cells. The TUNEL assay was used to detect apoptosis in mouse tumor cells. RESULTS: WTSP inhibited the proliferation of SK-NEP-1 cells in a dose- and time-dependent manner, and it arrested SK-NEP-1 cells in G2/M phase. WTSP-treated cells exhibited a low expression of PCNA and Bcl-2 and high expression of Bax. The expression of ß-catenin was markedly changed after WTSP treatment. WTSP-treated mice had significantly smaller tumors than untreated mice. CONCLUSION: Our findings indicated an anti-tumor effect of WTSP, which is correlated with Wnt/ß-catenin pathway. This newly identified peptide may exert a therapeutic effect of WT in the future.