Genome-wide 5-hydroxymethylcytosines in circulating cell-free DNA as noninvasive diagnostic markers for gastric cancer.

BACKGROUND: 5-Hydroxymethylcytosine-enriched gene profiles and regions show tissue-specific and tumor specific. There is a potential value to explore cell-free DNA 5-hydroxymethylcytosine feature biomarkers for early gastric cancer detection. METHODS: A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the different 5-hydroxymethylcytosine modification features of cell free DNA. Significantly differential 5-hydroxymethylcytosine modification genes were identified to construct a gastric cancer diagnostic model. Data set from GEO was used as an external testing set to test the robustness of the diagnostic model. RESULTS: Accounting for more than 90% of 5-hydroxymethylcytosine peaks were distributed in the gene body in both the gastric cancer and control groups. The diagnostic model was developed based on five different 5-hydroxymethylcytosine modification genes, FBXL7, PDE3A, TPO, SNTG2 and STXBP5. The model could effectively distinguish gastric cancer patients from controls in the training (AUC = 0.95, sensitivity = 88.6%, specificity = 94.3%), validation (AUC = 0.87, sensitivity = 73.3%, specificity = 93.3%) and testing (AUC = 0.90, sensitivity = 81.9%, specificity = 90.2%) sets. The risk scores of the controls from the model were significantly lower than those of gastric cancer patients in both our own data (P < 0.001) and GEO external testing data (P < 0.001), and no significant difference between different TNM stage patients (P = 0.09 and 0.66). Furthermore, there was no significant difference between the healthy control and benign gastric disease patients in the testing set from GEO (P = 0.10). CONCLUSIONS: The characteristics of 5-hydroxymethylcytosine in cell free DNA are specific to gastric cancer patients, and the diagnostic model constructed by five genes' 5-hydroxymethylcytosine features could effectively identify gastric cancer patients.

Appraising associations between signature lipidomic biomarkers and digestive system cancer risk: novel evidences from a prospective cohort study of UK Biobank and Mendelian randomization analyses.

BACKGROUND: The roles of serum lipids on digestive system cancer (DSC) risk were still inconclusive. In this study, we systematically assessed indicative effects of signature lipidomic biomarkers (high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)) on DSC (oesophagus, stomach, colorectal, liver, gallbladder, and pancreas cancers) risk. METHODS: HDL-C, LDL-C, and TG concentration measurements were respectively analyzed with enzyme immunoinhibition, enzymatic selective protection, and GPO-POD methods in AU5800 supplied from Beckman Coulter. The diagnoses of DSCs were coded using International Classification of Diseases, Tenth Revision (ICD-10) codes updated until October 2022 in the UK Biobank (UKB). In this study, we assessed phenotypic association patterns between signature lipidomic biomarkers and DSC risk using restricted cubic splines (RCSs) in multivariable-adjusted Cox proportional hazards regression models. Moreover, linear and nonlinear causal association patterns of signature lipidomic biomarkers with DSC risk were determined by linear and nonlinear Mendelian randomization (MR) analyses. RESULTS: A median follow-up time of 11.8 years was recorded for 319,568 participants including 6916 DSC cases. A suggestive independent nonlinear phenotypic association was observed between LDL-C concentration and stomach cancer risk (Pnonlinearity < 0.05, Poverall < 0.05). Meanwhile, a remarkable independent linear negative phenotypic association was demonstrated between HDL-C concentration and stomach cancer risk (Pnonlinearity > 0.05, Poverall < 0.008 (0.05/6 outcomes, Bonferroni-adjusted P)), and suggestive independent linear positive associations were observed between HDL-C concentration and colorectal cancer risk, and between TG concentration and gallbladder cancer risk (Pnonlinearity > 0.05, Poverall < 0.05). Furthermore, based on nonlinear and linear MR-based evidences, we observed an suggestive independent negative causal association (hazard ratio (HR) per 1 mmol/L increase: 0.340 (0.137-0.843), P = 0.020) between LDL-C and stomach cancer risk without a nonlinear pattern (Quadratic P = 0.901, Cochran Q P = 0.434). Meanwhile, subgroup and stratified MR analyses both supported the category of LDL-C ≥ 4.1 mmol/L was suggestively protective against stomach cancer risk, especially among female participants (HR: 0.789 (0.637-0.977), P = 0.030) and participants aged 60 years or older (HR: 0.786 (0.638-0.969), P = 0.024), and the category of TG ≥ 2.2 mmol/L concluded to be a suggestive risk factor for gallbladder cancer risk in male participants (HR: 1.447 (1.020-2.052), P = 0.038) and participants aged 60 years or older (HR: 1.264 (1.003-1.593), P = 0.047). CONCLUSIONS: Our findings confirmed indicative roles of signature lipidomic biomarkers on DSC risk, notably detecting suggestive evidences for a protective effect of high LDL-C concentration on stomach cancer risk, and a detrimental effect of high TG concentration on gallbladder cancer risk among given participants.

The roles of epigallocatechin gallate in the tumor microenvironment, metabolic reprogramming, and immunotherapy.

Cancer, a disease that modern medicine has not fully understood and conquered, with its high incidence and mortality, deprives countless patients of health and even life. According to global cancer statistics, there were an estimated 19.3 million new cancer cases and nearly 10 million cancer deaths in 2020, with the age-standardized incidence and mortality rates of 201.0 and 100.7 per 100,000, respectively. Although remarkable advancements have been made in therapeutic strategies recently, the overall prognosis of cancer patients remains not optimistic. Consequently, there are still many severe challenges to be faced and difficult problems to be solved in cancer therapy today. Epigallocatechin gallate (EGCG), a natural polyphenol extracted from tea leaves, has received much attention for its antitumor effects. Accumulating investigations have confirmed that EGCG can inhibit tumorigenesis and progression by triggering apoptosis, suppressing proliferation, invasion, and migration, altering tumor epigenetic modification, and overcoming chemotherapy resistance. Nevertheless, its regulatory roles and biomolecular mechanisms in the immune microenvironment, metabolic microenvironment, and immunotherapy remain obscure. In this article, we summarized the most recent updates about the effects of EGCG on tumor microenvironment (TME), metabolic reprogramming, and anti-cancer immunotherapy. The results demonstrated EGCG can promote the anti-cancer immune response of cytotoxic lymphocytes and dendritic cells (DCs), attenuate the immunosuppression of myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), and inhibit the tumor-promoting functions of tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), and various stromal cells including cancer-associated fibroblasts (CAFs), endothelial cells (ECs), stellate cells, and mesenchymal stem/stromal cells (MSCs). Additionally, EGCG can suppress multiple metabolic reprogramming pathways, including glucose uptake, aerobic glycolysis, glutamine metabolism, fatty acid anabolism, and nucleotide synthesis. Finally, EGCG, as an immunomodulator and immune checkpoint blockade, can enhance immunotherapeutic efficacy and may be a promising candidate for antitumor immunotherapy. In conclusion, EGCG plays versatile regulatory roles in TME and metabolic reprogramming, which provides novel insights and combined therapeutic strategies for cancer immunotherapy.

The crosstalk among the physical tumor microenvironment and the effects of glucose deprivation on tumors in the past decade.

The occurrence and progression of tumors are inseparable from glucose metabolism. With the development of tumors, the volume increases gradually and the nutritional supply of tumors cannot be fully guaranteed. The tumor microenvironment changes and glucose deficiency becomes the common stress environment of tumors. Here, we discuss the mutual influences between glucose deprivation and other features of the tumor microenvironment, such as hypoxia, immune escape, low pH, and oxidative stress. In the face of a series of stress responses brought by glucose deficiency, different types of tumors have different coping mechanisms. We summarize the tumor studies on glucose deficiency in the last decade and review the genes and pathways that determine the fate of tumors under harsh conditions. It turns out that most of these genes help tumor cells survive in glucose-deprivation conditions. The development of related inhibitors may bring new opportunities for the treatment of tumors.

The potential of epigallocatechin gallate in the chemoprevention and therapy of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), one of the most notorious malignancies globally, has a high fatality and poor prognosis. Though remarkable breakthroughs have been made in the therapeutic strategies recently, the overall survival of HCC remains unsatisfactory. Consequently, the therapy of HCC remains a great challenge. Epigallocatechin gallate (EGCG), a natural polyphenol extracted from the leaves of the tea bush, has been extensively investigated for its antitumor effects. In this review, we summarize the previous literature to elucidate the roles of EGCG in the chemoprophylaxis and therapy of HCC. Accumulating evidence has confirmed EGCG prevents and inhibits the hepatic tumorigenesis and progression through multiple biological mechanisms, mainly involving hepatitis virus infection, oxidative stress, proliferation, invasion, migration, angiogenesis, apoptosis, autophagy, and tumor metabolism. Furthermore, EGCG enhances the efficacy and sensitivity of chemotherapy, radiotherapy, and targeted therapy in HCC. In conclusion, preclinical studies have confirmed the potential of EGCG for chemoprevention and therapy of HCC under multifarious experimental models and conditions. Nevertheless, there is an urgent need to explore the safety and efficacy of EGCG in the clinical practice of HCC.

Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs.

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents' diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents' performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.

A cuproptosis-related lncRNA signature to predict prognosis and immune microenvironment of colon adenocarcinoma.

Cuproptosis is a novel cell death modality but its regulatory role in the colon cancer remains obscure. This study is committed to establishing a cuproptosis-related lncRNA (CRL) signature to forecast the prognosis for colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) samples were randomly divided into training and validation cohorts. LASSO-COX analysis was performed to construct a prognostic signature consisting of five CRLs (AC015712.2, ZEB1-AS1, SNHG26, AP001619.1, and ZKSCAN2-DT). We found the patients with high-risk scores suffered from poor prognosis in training cohort (p < 0.001) and validation cohort (p = 0.004). Nomogram was created based on the 5-CRL signature. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) demonstrated the nomogram performed well in 1­, 3­, and 5­year overall survival (OS). Subsequently, we observed increased infiltration of multiple immune cells and upregulated expression of immune checkpoints and RNA methylation modification genes in high-risk patients. Additionally, gene set enrichment analysis (GSEA) revealed two tumor-related pathways, including MAPK and Wnt signaling pathways. Finally, we found AKT inhibitors, all-trans retinoic acid (ATRA), camptothecin, and thapsigargin had more sensitivity to antitumor therapy in high-risk patients. Collectively, this CRL signature is promising for the prognostic prediction and precise therapy of COAD.

Contrastive domain adaptation with consistency match for automated pneumonia diagnosis.

Pneumonia can be difficult to diagnose since its symptoms are too variable, and the radiographic signs are often very similar to those seen in other illnesses such as a cold or influenza. Deep neural networks have shown promising performance in automated pneumonia diagnosis using chest X-ray radiography, allowing mass screening and early intervention to reduce the severe cases and death toll. However, they usually require many well-labelled chest X-ray images for training to achieve high diagnostic accuracy. To reduce the need for training data and annotation resources, we propose a novel method called Contrastive Domain Adaptation with Consistency Match (CDACM). It transfers the knowledge from different but relevant datasets to the unlabelled small-size target dataset and improves the semantic quality of the learnt representations. Specifically, we design a conditional domain adversarial network to exploit discriminative information conveyed in the predictions to mitigate the domain gap between the source and target datasets. Furthermore, due to the small scale of the target dataset, we construct a feature cloud for each target sample and leverage contrastive learning to extract more discriminative features. Lastly, we propose adaptive feature cloud expansion to push the decision boundary to a low-density area. Unlike most existing transfer learning methods that aim only to mitigate the domain gap, our method instead simultaneously considers the domain gap and the data deficiency problem of the target dataset. The conditional domain adaptation and the feature cloud generation of our method are learning jointly to extract discriminative features in an end-to-end manner. Besides, the adaptive feature cloud expansion improves the model's generalisation ability in the target domain. Extensive experiments on pneumonia and COVID-19 diagnosis tasks demonstrate that our method outperforms several state-of-the-art unsupervised domain adaptation approaches, which verifies the effectiveness of CDACM for automated pneumonia diagnosis using chest X-ray imaging.

Genetic estimation of correlations and causalities between multifaceted modifiable factors and gastro-oesophageal reflux disease.

Background: Gastro-oesophageal reflux disease (GORD) is a common gastrointestinal dysfunction that significantly affects the quality of daily life, and health interventions are challenging to prevent the risk of GORD. In this study, we used Mendelian randomization framework to genetically determine the causal associations between multifaceted modifiable factors and the risk of GORD. Materials and methods: Sixty-six exposures with available instrumental variables (IVs) across 6 modifiable pathways were included in the univariable MR analysis (UVMR). Summary-level genome-wide association studies (GWAS) datasets for GORD were retrieved from the Neale Lab (GORD Neale , Ncases = 29975, Ncontrols = 390556) and FinnGen (GORD Finn , Ncases = 13141, Ncontrols = 89695). Using the METAL software, meta-analysis for single nucleotide polymorphisms (SNPs) from GORD Neale and GORD Finn was conducted with an inverse variance weighted (IVW) fixed-effect model. Moreover, we leveraged partition around medoids (PAM) clustering algorithm to cluster genetic correlation subtypes, whose hub exposures were conditioned for multivariable MR (MVMR) analyses. P-values were adjusted with Bonferroni multiple comparisons. Results: Significant causal associations were identified between 26 exposures (15 risk exposures and 11 protective exposures) and the risk of GORD. Among them, 13 risk exposures [lifetime smoking, cigarette consumption, insomnia, short sleep, leisure sedentary behavior (TV watching), body mass index (BMI), body fat percentage, whole body fat mass, visceral adipose tissue, waist circumference, hip circumference, major depressive disorder, and anxious feeling], and 10 protective exposures (leisure sedentary behavior (computer use), sitting height, hand grip strength (left and right), birth weight, life satisfaction, positive affect, income, educational attainment, and intelligence) showed novel significant causal associations with the risk of GORD. Moreover, 13 exposures still demonstrated independent associations with the risk of GORD following MVMR analyses conditioned for hub exposures (educational attainment, smoking initiation and BMI). In addition, 12 exposures showed suggestive causal associations with the risk of GORD. Conclusion: This study systematically elucidated the modifiable factors causally associated with the risk of GORD from multifaceted perspectives, which provided implications for prevention and treatment of GORD.

PTEN Expression Was Significantly Associated with PD-L1 Score but Not with EBV Infection in Gastric Cancer.

Purpose: Gastric cancer (GC) remains a prevalent aggressive tumor with high morbidity and mortality globally. The identification of GC subtypes based on molecular features improved the prediction of prognosis and the selection of targeted therapies. PTEN is a characteristic tumor suppressor, while its association with different GC subtypes was unknown. Patients and Methods: The cohort consisted of 248 patients diagnosed with gastric cancer who were hospitalized and received radical gastrectomy. In addition, PTEN gene expression matrix of STAD was retrieved from TCGA. The mRNA and protein levels of PTEN and PD-L1 were detected using qRT-PCR and IHC staining. Multivariate logistic regression and Kaplan-Meier analysis were used to examine the relationship between PTEN expression and clinical characteristics. Results: In our study, PTEN was downregulated in gastric tumors both in mRNA and protein levels. Its inactivation was closely linked to higher histological grade (P = 0.005), neural invasion (P = 0.012), depth of invasion (P = 0.021), lymph metastasis (P = 0.026), and TNM stage (P = 0.001) of GC in the present study. Moreover, according to the molecular subtypes, high PTEN expression was related to high TPS score of PD-L1 positively (P = 0.010) but was not associated with MSI and EBV infection. Further, TCGA data validated that PTEN was indeed correlated with histological grade and invasion depth and positively related to PD-L1 expression (R = 0.29, adjusted P < 0.001). Conclusion: The above results suggested that PTEN expression was a useful marker in gastric carcinogenesis and progression and in the selection of immunotherapy-based treatments for GC patients.

Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs.

(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the "live" dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift.

[Corrigendum] Rab22a is a novel prognostic marker for cell progression in breast cancer.

Following the publication of this article, the authors have realized that Table I contained an error: The number of patients who were alive in the Rab22a high expression group should have been written as 77 instead of 772.
A corrected version of the Table is shown on the next page (the corrected datum is highlighted in bold). The authors sincerely apologize for the error that was introduced during the preparation of this Table, and regret any inconvenience that this mistake has caused. [the original article was published in International Journal of Molecular Medicine 45: 1037-1046, 2020; DOI: 10.3892/ijmm.2020.4486].

Lipocalin-1 Expression as a Prognosticator Marker of Survival in Breast Cancer Patients.

PURPOSE: LCN1 (lipocalin-1), a gene that encodes tear lipocalin (or von Ebner's gland protein), is mainly expressed in secretory glands and tissues, such as the lachrymal and lingual gland, and nasal, mammary, and tracheobronchial mucosae. Analysis of the Cancer Genome Atlas (TCGA) Breast Carcinoma (BRCA) level 3 data revealed a relationship between LCN1 expression and survival in breast cancer patients. METHODS: The χ2 test and Fisher exact test were applied to analyze the clinical data and RNA sequencing expression data, and the association between LCN1 expression and clinicopathologic features was determined. The receiver-operating characteristic (ROC) curve of LCN1 was drawn to assess its ability as a diagnostic marker, and the optimal cutoff value was obtained from the ROC curve to distinguish groups with high and low LCN1 expression. Cox regression was used to compare both groups, and a log-rank test was applied to calculate p values and compare the -Kaplan-Meier curves. Furthermore, GEO datasets were employed for external data validation. RESULTS: Analysis of 1,104 breast cancer patients with a primary tumor revealed that LCN1 was overexpressed in breast cancer. High LCN1 expression was associated with clinicopathologic features and poor survival. Analyzing the area under the ROC curve (AUC) of LCN1, it was found that its diagnostic ability was limited. Multivariate analysis indicated that LCN1 expression is an independent predictor of survival in breast cancer patients. Through validation in GEO datasets, LCN1 expression was higher in tumor than normal tissue of the breast. High LCN1 expression was associated with poor survival in breast cancer patients. CONCLUSIONS: High LCN1 expression is an independent prognosticator of a poor prognosis in breast cancer.

Rab22a is a novel prognostic marker for cell progression in breast cancer.

Breast cancer (BC) is the most common female malignant tumor worldwide. The mechanism of tumorigenesis is still unclear. Ras­related proteins in brain (Rab)22a belongs to the Ras superfamily, which may act as an oncogene and participate in carcinogenesis. The present study aims to identify whether Rab22a could be a novel biomarker of prognosis and determine the effects of Rab22a on BC cell progression. A total 258 BC and 56 para­tumor or non­tumor formalin fixed paraffin embedded tissues were stained through immunohistochemistry. The association between Rab22a expression and clinicopathological features, as well as overall survival status were analyzed. The expression level of Rab22a in breast cell lines were detected using reverse transcription­quantitative PCR and western blotting. SK­BR­3 cells were infected with Rab22a short hairpin RNA lenti­virus and the ability of cell proliferation, migration and invasion were measured. Gene Set Enrichment Analysis (GSEA) was employed to analyze the pathways involved in the Rab22a mRNA high level group. Rab22a was found to be overexpressed in BC tissues and upregulated in BC cells. High expression of Rab22a was related to a poor prognosis of patients with BC. Knockdown of Rab22a decreased the proliferation, migration and invasion ability of BC cells. GSEA indicated that certain pathways, including mammalian target of rapamycin complex 1 and protein secretion were upregulated, while pathways, such as hypoxia and KRas were downregulated in the Rab22a high level group. Rab22a is of prognostic value for BC and necessary for BC cell proliferation.

A new prognostic factor of breast cancer: High carboxyl ester lipase expression related to poor survival.

OBJECTIVE: The enzyme carboxyl ester lipase (CEL), known as bile salt-dependent lipase (BSDL) or bile salt-stimulated lipase (BSSL), is mainly expressed in pancreatic acinar cells and lactating mammary glands. To investigate the link between CEL expression of breast cancer (BC) tissues and the survival of BC patients by analyzing The Cancer Genome Atlas Breast Carcinoma (TCGA-BRCA) level 3 data. METHODS: The clinical information and RNA-sequencing (RNA-Seq) expression data were downloaded from TCGA. Patients were divided into a high CEL expression group and a low CEL expression group using the optimal cutoff value (5.611) identified from the ROC curve. Chi-square test and Fisher exact test were used to find the correlation between the expression of CEL and clinicopathologic features. To assess the diagnostic capability, the receiver operating characteristic (ROC) curve of CEL was drawn. The survival differences between high and low CEL expression groups were compared by Cox regression analysis. Log-rank test was applied to the calculation of p values and the comparison of the Kaplan-Meier curves. Furthermore, Gene Expression Omnibus (GEO) datasets were used for external data validation. RESULTS: Analysis of 1104 cases of tumor data showed that CEL was over-expressed in breast cancer. There were relationships between high CEL expression and clinicopathologic features. The high CEL expression group had a lower survival. By analyzing the area under the ROC curve (AUC) of CEL, it was found to have a limited diagnostic capability. CEL expression may be an independent prognostic factor for breast cancer survival through the multivariate analysis. The validation in GEO datasets also showed that CEL expression was higher in breast tumor tissues than in normal breast tissues. High CEL expression was associated with the poor overall survival of breast cancer. CONCLUSIONS: High CEL expression may be an independent prognostic factor for the poor survival of breast cancer.

Iron oxide nanoparticles promote the migration of mesenchymal stem cells to injury sites.

BACKGROUND: Developing new methods to deliver cells to the injured tissue is a critical factor in translating cell therapeutics research into clinical use; therefore, there is a need for improved cell homing capabilities. MATERIALS AND METHODS: In this study, we demonstrated the effects of labeling rat bone marrow-derived mesenchymal stem cells (MSCs) with fabricated polydopamine (PDA)-capped Fe3O4 (Fe3O4@PDA) superparticles employing preassembled Fe3O4 nanoparticles as the cores. RESULTS: We found that the Fe3O4@PDA composite superparticles exhibited no adverse effects on MSC characteristics. Moreover, iron oxide nanoparticles increased the number of MSCs in the S-phase, their proliferation index and migration ability, and their secretion of vascular endothelial growth factor relative to unlabeled MSCs. Interestingly, nanoparticles not only promoted the expression of C-X-C chemokine receptor 4 but also increased the expression of the migration-related proteins c-Met and C-C motif chemokine receptor 1, which has not been reported previously. Furthermore, the MSC-loaded nanoparticles exhibited improved homing and anti-inflammatory abilities in the absence of external magnetic fields in vivo. CONCLUSION: These results indicated that iron oxide nanoparticles rendered MSCs more favorable for use in injury treatment with no negative effects on MSC properties, suggesting their potential clinical efficacy.