Elevated BCAA Suppresses the Development and Metastasis of Breast Cancer.

Branched-chain amino acids (BCAAs) are the three essential amino acids including leucine, isoleucine, and valine. BCAA metabolism has been linked with the development of a variety of tumors. However, the impact of dietary BCAA intake on breast tumor progression and metastasis remains to be fully explored. Here, we unexpectedly find that the elevated BCAA, either in the genetic model or via increasing dietary intake in mice, suppresses the tumor growth and lung metastasis of breast cancer. The survival analysis shows that BCAA catabolic gene expression is strongly associated with long-term oncological outcomes in patients with breast cancer. In Pp2cm knockout mice in which BCAAs accumulate due to the genetic defect of BCAA catabolism, the breast tumor growth is suppressed. Interestingly, while the cell proliferation and tumor vasculature remain unaffected, more cell death occurs in the tumor in Pp2cm knockout mice, accompanied with increased natural killer (NK) cells. Importantly, increasing BCAA dietary intake suppresses breast tumor growth in mice. On the other hand, there are fewer lung metastases from primary breast tumor in Pp2cm knockout mice and the high BCAA diet-fed mice, suggesting high BCAA also suppresses the lung metastasis of breast cancer. Furthermore, low BCAA diet promotes lung colonization of breast cancer cells in tail vein model. The migration and invasion abilities of breast cancer cells are impaired by high concentration of BCAA in culture medium. The suppressed tumor metastasis and cell migration/invasion abilities by elevated BCAA are accompanied with reduced N-cadherin expression. Together, these data show high BCAA suppresses both tumor growth and metastasis of breast cancer, demonstrating the potential benefits of increasing BCAA dietary intake in the treatment of breast cancer.

DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules.

Objectives: Distinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction. Methods: A total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled. The different radiological manifestations were identified mainly as various nodules densities and morphological features. Then, these nodules were classified into benign and malignant groups, both of which were subdivided into finer specific pathological types. Here, we proposed a deep convolutional neural network for the assessment of lung nodules named DeepLN to identify the radiological features and predict the pathologic subtypes of pulmonary nodules. Results: In terms of density, the area under the receiver operating characteristic curves (AUCs) of DeepLN were 0.9707 (95% confidence interval, CI: 0.9645-0.9765), 0.7789 (95%CI: 0.7569-0.7995), and 0.8950 (95%CI: 0.8822-0.9088) for the pure-ground glass opacity (pGGO), mixed-ground glass opacity (mGGO) and solid nodules. As for the morphological features, the AUCs were 0.8347 (95%CI: 0.8193-0.8499) and 0.9074 (95%CI: 0.8834-0.9314) for spiculation and lung cavity respectively. For the identification of malignant nodules, our DeepLN algorithm achieved an AUC of 0.8503 (95%CI: 0.8319-0.8681) in the test set. Pertaining to predicting the pathological subtypes in the test set, the multi-task AUCs were 0.8841 (95%CI: 0.8567-0.9083) for benign tumors, 0.8265 (95%CI: 0.8004-0.8499) for inflammation, and 0.8022 (95%CI: 0.7616-0.8445) for other benign ones, while AUCs were 0.8675 (95%CI: 0.8525-0.8813) for lung adenocarcinoma (LUAD), 0.8792 (95%CI: 0.8640-0.8950) for squamous cell carcinoma (LUSC), 0.7404 (95%CI: 0.7031-0.7782) for other malignant ones respectively in the malignant group. Conclusions: The DeepLN based on deep learning algorithm represented a competitive performance to predict the imaging characteristics, malignancy and pathologic subtypes on the basis of non-invasive CT images, and thus had great possibility to be utilized in the routine clinical workflow.

TNFAIP3 Interacting Protein 3 Is an Activator of Hippo-YAP Signaling Protecting Against Hepatic Ischemia/Reperfusion Injury.

BACKGROUND AND AIMS: Hepatic ischemia/reperfusion (I/R) injury, a common clinical problem that occurs during liver surgical procedures, causes a large proportion of early graft failure and organ rejection cases. The identification of key regulators of hepatic I/R injury may provide potential strategies to clinically improve the prognosis of liver surgery. Here, we aimed to identify the role of tumor necrosis factor alpha-induced protein 3-interacting protein 3 (TNIP3) in hepatic I/R injury and further reveal its immanent mechanisms. APPROACH AND RESULTS: In the present study, we found that hepatocyte TNIP3 was markedly up-regulated in livers of both persons and mice subjected to I/R surgery. Hepatocyte-specific Tnip3 overexpression effectively attenuated I/R-induced liver necrosis and inflammation, but improved cell proliferation in mice, whereas TNIP3 ablation largely aggravated liver injury. This inhibitory effect of TNIP3 on hepatic I/R injury was found to be dependent on significant activation of the Hippo-YAP signaling pathway. Mechanistically, TNIP3 was found to directly interact with large tumor suppressor 2 (LATS2) and promote neuronal precursor cell-expressed developmentally down-regulated 4-mediated LATS2 ubiquitination, leading to decreased Yes-associated protein (YAP) phosphorylation at serine 112 and the activated transcription of factors downstream of YAP. Notably, adeno-associated virus delivered TNIP3 expression in the liver substantially blocked I/R injury in mice. CONCLUSIONS: TNIP3 is a regulator of hepatic I/R injury that alleviates cell death and inflammation by assisting ubiquitination and degradation of LATS2 and the resultant YAP activation.TNIP3 represents a promising therapeutic target for hepatic I/R injury to improve the prognosis of liver surgery.

Timing of chronic obstructive pulmonary disease diagnosis in lung cancer prognosis: a clinical and genomic-based study.

BACKGROUND: A two-phase study (clinical and genomic-based) was conducted to evaluate the effect of timing of chronic obstructive pulmonary disease (COPD) diagnosis on lung cancer outcomes. METHODS: The prognostic influence of COPD was investigated in a clinical cohort of 1,986 patients who received surgery for stage I lung cancer; 823 (41.4%) of them also had COPD, including 549 (27.6%) incidental COPD (diagnosed within 6-months of lung cancer diagnosis) and 274 (13.8%) prior COPD (>6 months before lung cancer diagnosis). The genomic variations were analyzed from another cohort of 1,549 patients for association with 384 lung cancer-related single nucleotide polymorphisms (SNPs). RESULTS: Older age (≥70 years), smokers, and respiratory symptoms were independent predictors of incidental COPD in lung cancer (all P<0.05). Similar to prior COPD, incidental COPD increased postoperative complications and worsened quality-of-life related to dyspnea (both P<0.05). Multivariate Cox regression analysis showed lung cancer survival decreased significantly in incidental COPD (HR, 1.30; 95% CI, 1.02-1.66), but not in prior COPD (HR, 1.15; 95% CI, 0.87-1.52). Among prior COPD, median survival showed a trend for being better in those with fewer exacerbations (0-1 vs. ≥2 exacerbation/year; 6.1 vs. 4.1 years; P=0.10). The SNP-based analysis identified ADCY2:rs52827085 was significantly associated with risk of incidental COPD (OR, 1.76; 95% CI, 1.30-2.38) and NRXN1:rs1356888 associated with prior COPD complicated with lung cancer (OR, 1.73; 95% CI, 1.29-2.33). CONCLUSIONS: Different long-term survival and genomic variants were observed between lung cancer patients with incidental and with prior COPD, suggesting timing of COPD diagnosis should be considered in lung cancer clinical management and mechanistic research.

Prognostic nomogram for predicting long-term cancer-specific survival in patients with lung carcinoid tumors.

BACKGROUND: Lung carcinoid is a rare malignant tumor with poor survival. The current study established a nomogram model for predicting cancer-specific survival (CSS) in patients with lung carcinoid tumors. METHODS: A total of 1956 patients diagnosed with primary lung carcinoid tumors were extracted from the Surveillance, Epidemiology, and End Results database. The specific predictors of CSS for lung carcinoid tumors were identified and integrated to build a nomogram. Validation of the nomogram was conducted using parameters concordance index (C-index), calibration plots, decision curve analyses (DCAs), and the receiver operating characteristic (ROC) curve. RESULTS: Age at diagnosis, grade, histological type, N stage, M stage, surgery of the primary site, radiation of the primary site, and tumor size were independent prognostic factors of CSS. High discriminative accuracy of the nomogram model was shown in the training cohort (C-index = 0.873), which was also testified in the internal validation cohort (C-index = 0.861). In both cohorts, the calibration plots showed good concordance between the predicted and observed CSS at 3, 5, and 10 years. The DCA showed great potential for clinical application. The ROC curve showed superior survival predictive ability of the nomogram model (area under the curve = 0.868). CONCLUSIONS: We developed a practical nomogram that provided independent predictions of CSS for patients with lung carcinoid tumors. This nomogram may have the potential to assist clinicians in prognostic evaluations or developing individualized therapies for patients with this neoplasm.

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images.

OBJECTIVE: The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases. METHODS: Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients. RESULTS: The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94-0.98), 0.80 (95% CI: 0.72-0.88), and 0.73 (95% CI: 0.63-0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71). CONCLUSION: In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.

Low T790M relative allele frequency indicates concurrent resistance mechanisms and poor responsiveness to osimertinib.

BACKGROUND: T790M relative allele frequency (RAF) in plasma, calculated by the ratio of T790M to epidermal growth factor receptor (EGFR)-sensitizing mutation allele frequencies (AF), is associated with osimertinib response in patients with progressive non-small cell lung cancer (NSCLC) post 1st generation EGFR-tyrosine kinase inhibitor (TKI) treatment. However, which subgroup of patients carry concurrent resistance mechanisms and have poor responsiveness to osimertinib remains unknown. METHODS: Matched re-biopsy tissue and plasma samples obtained from 32 patients who had progression following 1st generation EGFR-TKI treatment were genotyped using next-generation sequencing (NGS) to investigate which subgroup of patients, classified by plasma position 790 (T790M) RAF, were more likely to carry concurrent resistance mechanisms. In another independent cohort, consisting of 21 T790M-positive patients, we validated whether these patients had a poor response to osimertinib treatment. RESULTS: In the discovery cohort, patients with T790M RAF less than 20% were more likely to harbor concurrent resistance mechanisms (P=0.018), such as MET or ERBB2 amplification, and small cell lung cancer transformation. In the validation cohort, we found that patients with low T790M RAF (<20%) had significantly lower objective response rates (ORRs) (0 vs. 68.8%, P=0.03) and disease control rates (DCRs) (60% vs. 100%, P=0.048) in response to osimertinib compared to patients with high T790M RAF. CONCLUSIONS: In patients with progressive NSCLC post 1st generation EGFR-TKI treatment, plasma T790M RAFs of less than 20% can be used to identify patients who carry concurrent resistance mechanisms, and can predict a poorer response to osimertinib. TRIAL REGISTRATION: This study was registered on http://www.chictr.org.cn (registration number: ChiCTR-DDD-16007900).

Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). METHODS: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. RESULTS: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. CONCLUSION: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

IU1 suppresses proliferation of cervical cancer cells through MDM2 degradation.

Previous studies have demonstrated that the antitumor potential of IU1 (a pharmacological compound), which was mediated by selective inhibition of proteasome-associated deubiquitinase ubiquitin-specific protease 14 (USP14). However, the underlying molecular mechanisms remain elusive. It has been well established that mdm2 (Murine double minute 2) gene was amplified and/or overexpressed in a variety of human neoplasms, including cervical cancer. Furthermore, MDM2 is critical to cervical cancer development and progression. Relatively studies have reported that USP15 and USP7 stabilized MDM2 protein levels by removing its ubiquitin chain. In the current study, we studied the cell proliferation status after IU1 treatment and the USP14-MDM2 protein interaction in cervical cancer cells. This study experimentally revealed that IU1 treatment reduced MDM2 protein expression in HeLa cervical cancer cells, along with the activation of autophagy-lysosomal protein degradation and promotion of ubiquitin-proteasome system (UPS) function, thereby blocked G0/G1 to S phase transition, decreased cell growth and triggered cell apoptosis. Thus, these results indicate that IU1 treatment simultaneously targets two major intracellular protein degradation systems, ubiquitin-proteasome and autophagy-lysosome systems, which leads to MDM2 degradation and contributes to the antitumor effect of IU1.

A nomogram for predicting cancer-specific survival in patients with osteosarcoma as secondary malignancy.

The prognostic factors for survival among patients with secondary osteosarcoma remain unclear. The aim of this study was to develop a practical nomogram for predicting cancer-specific survival (CSS) in patients with osteosarcoma as a secondary malignancy. The surveillance, epidemiology, and end results database was used for the identification of osteosarcoma cases. The total sample comprised 5860 cases of primary osteosarcoma and 268 cases of secondary osteosarcoma during the period from 1973 to 2015. The CSS and overall survival (OS) of primary and secondary osteosarcomas were analyzed. The predictors of CSS for secondary osteosarcoma were identified and integrated to build a nomogram. Validation of the nomogram was performed using concordance index (C-index) and calibration plots. The results indicated that patients with secondary osteosarcoma had poorer CSS and OS than patients with primary osteosarcoma. The nomogram model exhibited high discriminative accuracy in the training cohort (C-index = 0.826), which was confirmed in the internal validation cohort (C-index = 0.791). In addition, the calibration plots confirmed good concordance for prediction of CSS at 3, 5, and 10 years. In conclusion, we developed a practical nomogram that provided individual predictions of CSS for patients with secondary osteosarcoma. This nomogram may help clinicians with prognostic evaluations and with the development of individualized therapies for this aggressive disease.

The Wnt/ß-catenin/VASP positive feedback loop drives cell proliferation and migration in breast cancer.

Previous studies have shown that the main function of VASP is to regulate the cytoskeleton and play an important role in promoting tumor cell metastasis. In this study, we first reveal that VASP is located in the nucleus of breast cancer cells and elucidate a Wnt/ß-catenin/VASP positive feedback loop. We identify that VASP is a target gene of Wnt/ß-catenin signaling pathway, and activation of Wnt/ß-catenin signaling pathway can significantly upregulate VASP protein expression, while upregulated VASP protein can in turn promote translocation of ß-catenin and DVL3 proteins into the nucleus. In the nucleus, VASP, DVL3, ß-catenin, and TCF4 can form VASP/DVL3/ß-catenin/TCF4 protein complex, activating Wnt/ß-catenin signaling pathway, and promoting the expression of target genes VASP, c-myc, and cyclin D1. Thus, our study reveals that there is a Wnt/ß-catenin/VASP malignant positive feedback loop in breast cancer, which promotes the proliferation and migration of breast cancer cells, and breaking this positive feedback loop may provide new strategy for breast cancer treatment.

BMX activates Wnt/ß-catenin signaling pathway to promote cell proliferation and migration in breast cancer.

BACKGROUND: Breast cancer has become a dangerous killer for the female, which seriously threatened women's life, leading to huge pressures to society. The present study assessed the mechanism underlying the involvement of bone marrow tyrosine kinase on chromosome X (BMX) in breast cancer development. METHODS: The expression of BMX was examined by qPCR and immunohistochemistry. The effect of BMX on cell proliferation and migration was detected by Clone formation assay and Transwell assay. In vitro study, the correlation of BMX with Wnt/ß-catenin pathway was explored by western blot and TOP/FOP flash assay. RESULTS: In the present study, we found that BMX was up-regulated in breast cancer, which was associated with the tumor differentiation and TNM stage. Oncogenic BMX enhanced the ability of breast cancer cell proliferation and migration. Furthermore, BMX could up-regulate the protein expression levels of p-ß-catenin (Y142), p-ß-catenin(Y654) and inhibit the expression level of p-ß-catenin (S33/37), thus activating Wnt/ß-catenin pathway in MCF-7 and MDA-MB-231 cells. In addition, we revealed that BMX promoted GSK3ß phosphorylation, which suppressed the degradation of ß-catenin. CONCLUSIONS: In this study, we identified that BMX-activated Wnt/ß-catenin signaling pathway, playing an oncogenic role in breast cancer, suggesting that BMX could become a potential treatment target of breast cancer.

CREB1/Lin28/miR-638/VASP Interactive Network Drives the Development of Breast Cancer.

Breast cancer is one of the most common malignant tumors worldwide. Metastasis remains the leading cause of death in breast cancer patients. Research on the mechanism of breast cancer metastasis has become a core issue in breast cancer research. Our previous series of studies have shown that VASP, as a key oncogene, plays an important role in the development of various tumors such as breast cancer. In this study, we find that miR-638 can target to inhibit VASP expression, and Lin28 acts as an RNA-binding protein to regulate the processing of miR-638, which inhibits its maturation and promotes the expression of VASP. In addition, we also find that CREB1 acts as a transcription factor that binds to the promoter of Lin28 gene and activates the Lin28/miR-638/VASP pathway. Furthermore, CREB1 can also directly bind to the promoter of VASP, and activate VASP expression, forming a CREB/Lin28/miR-638/VASP interactive network, which plays an important role in promoting cell proliferation and migration in breast cancer. Our study explained the mechanism of CREB1/Lin28/miR-638/VASP network promoting the development of breast cancer, which further elucidated the mechanism of VASP as a key oncogene, and also provided a theoretical basis for expanding new approaches to tumor biotherapy.

Prognostic nomogram predicts overall survival in pulmonary large cell neuroendocrine carcinoma.

BACKGROUND: Large cell neuroendocrine carcinoma (LCNEC) is a rare and typically aggressive malignancy with poor prognosis. This study developed a nomogram model to predict the overall survival (OS) of patients with LCNEC. METHODS: LCNEC patients were identified from the Surveillance, Epidemiology, and End Results database between 2004-2014. Univariate and multivariate Cox regression models were used to determine demographic and clinicopathological features associated with OS. A nomogram model was generated to predict OS and its performance was assessed by Harrell's concordance index (C-index), calibration plots, and subgroup analysis by risk scores. RESULTS: Of 3048 eligible patients with LCNEC, 2138 were randomly grouped into the training set and 910 into the validation set. Age at diagnosis, gender, tumor stage, N stage, tumor size, and surgery of primary site were independent prognostic factors of OS. C-index values of the nomogram were 0.75 (95% CI, 0.74-0.76) and 0.76 (95% CI, 0.74-0.77) in the training and validation sets, respectively. In both cohorts, the calibration plots showed good concordance between the predicted and observed OS at 3 and 5 years. Kaplan-Meier curves revealed significant differences in OS in patients stratified by nomogram-based risk score, and patients with a higher-than-median risk score had poorer OS. CONCLUSION: This is the first nomogram developed and validated in a large population-based cohort for predicting OS in patients with LCNEC, and it shows favorable discrimination and calibration abilities. Use of this proposed nomogram has the potential to improve prediction of survival risk, and lead to individualized clinical decisions for LCNEC.

A novel oncogene TRIM63 promotes cell proliferation and migration via activating Wnt/ß-catenin signaling pathway in breast cancer.

The development of breast cancer is still a relatively unclear biological process, and there is currently no consensus on the occurrence of breast cancer and the process of tumor metastases. This study was to reveal a correlation between TRIM63 and the development of breast cancer. In this study, we found that the expression of TRIM63 was significantly increased in breast cancer tissues and closely related to pathological differentiation and TNM stage of breast cancer. Overexpression of TRIM63 could significantly promote proliferation and migration of breast cancer cells, while TRIM63 knockdown significantly inhibited the proliferation and migration of breast cancer cells. In addition, TRIM63 could activate Wnt/ß-catenin signaling pathway in breast cancer cells. Further study found that TRIM63 could regulate ß-catenin degradation by promoting GSK3ß phosphorylation. Our study revealed that TRIM63, as an oncogene, involved in breast cancer progression by activating the Wnt/ß-catenin signaling pathway, suggesting that the potential applicability of TRIM63 as a target for breast cancer treatment.

Silencing lncRNA SNHG6 suppresses proliferation and invasion of breast cancer cells through miR-26a/VASP axis.

The important role of LncRNA in the development of breast cancer is attracting more and more attention. In the previous study, we found that the expression level of LncRNA SNHG6 in breast cancer tissues and cells was significantly increased, but its mechanism in the development of breast cancer was still unclear. Our study found that knockdown of SNHG6 significantly inhibited the proliferation, migration and invasion of breast cancer cells MCF-7 and MDA-MB-231 cells. Further study showed that knockdown of SNHG6 significantly inhibited the expression level of VASP. More importantly, SNHG6 and VASP both can bind directly to miR-26a, suggesting that SNHG6 could act as a ceRNA to sponge miR-26a, thereby promoting the expression of VASP, which leading to activated proliferation, migration and invasion of breast cancer cells. Taken together, this study revealed the important role of the SNHG6/miR-26a/VASP regulatory network in the development of breast cancer, and provided a reference for exploring new pathogenesis and biomarkers of breast cancer.

Antitumor effects of saikosaponin b2 on breast cancer cell proliferation and migration.

Saikosaponin b2 (SSb2) can be extracted from Bupleurum spp. roots (Radix Bupleuri), which belongs to the Umbelliferae family. The current study aimed to explore the effects of SSb2 on proliferation of breast cancer cells and to identify the mechanism by which SSb2 affects breast cancer cell migration. mRNA expression levels of STAT3 and vasodilator­stimulated phosphoprotein (VASP) were determined and increased expression was observed in 16 breast cancer tissues compared with the paracancerous tissues. MTT, wound healing, colony formation assays and western blot suggested that SSb2 inhibited MCF­7 proliferation and migration. It was further identified by western blot analysis that SSb2 treatment reduced levels of phosphorylated STAT3, VASP, matrix metallopeptidase (MMP) 2 and MMP9 in MCF­7 compared with the untreated cells. In addition, it was demonstrated that inhibition of STAT3 phosphorylation decreased VASP expression levels and induction of STAT3 phosphorylation increased VASP levels. Furthermore, it was observed that the treatment of Kunming mice with SSb2 at 30 mg/kg/day for 30 days induced no obvious changes in the liver or kidney tissues, as determined by haematoxylin and eosin staining. In conclusion, these results indicated that SSb2 may be a potential antitumor drug for the treatment of breast cancer, which acts by suppressing proliferation and migration by downregulating the STAT3 signalling pathway and inhibiting the expression of VASP, MMP2 and MMP9 expression.

In Silico Detection and Characterization of microRNAs and Their Target Genes in microRNA Microarray Datasets from Patients with Systemic Sclerosis-Interstitial Lung Disease.

Interstitial lung disease (ILD) is the main reason of death in patients with systemic sclerosis (SSc). The potential microRNA (miRNA)-messenger RNA (mRNA) interaction networks of SSc-ILD from a systematic biological perspective are unclear. To characterize differentially expressed miRNAs (DE-miRNAs) and differentially expressed genes (DEGs) likely related to SSc-ILD, we downloaded the miRNA microarray dataset (GSE81923) and mRNA datasets (GSE76808 and GSE81292) from the Gene Expression Omnibus database. Comprehensive bioinformatic analyses were conducted to predict target genes for DE-miRNAs and generate an miRNA-hub gene network with SSc-ILD. In total, 26 DE-miRNAs were detected in SSc-ILD, among which 2 were upregulated and 24 were downregulated. Additionally, 178 common DEGs (55 upregulated and 123 downregulated) were identified. miRNAs were primarily enriched in pathways involving inflammation and regulation of fibroblasts. The hub genes identified were MMP7, IER2, HBEGF, CCL4, NFKBIA, JUNB, LIF, SERPINE1, FOSL1, and NAMPT. We discovered the miRNA-mediated regulatory network in SSc-ILD using an integrated bioinformatic analysis. The findings provide novel insight and expand our comprehension of the molecular mechanisms participating in the pathogenesis of SSc-ILD, along with identification of new potential diagnostic biomarkers.