Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis.

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.

The small nucleolar RNA SNORA51 enhances breast cancer stem cell-like properties via the RPL3/NPM1/c-MYC pathway.

Breast cancer stem cells (BCSCs) are key players in carcinogenesis and development. Small nucleolar RNAs (snoRNAs) seem to have a crucial influence on regulating stem cell-like properties in various cancers, but the underlying mechanism in breast cancer has not been determined. In this study, we first found that the expression of SNORA51 might be strongly and positively related to BCSCs-like properties. SNORA51 expression was assessed in breast cancer tissues (n = 158 patients) by in situ hybridization. Colony formation, cell counting kit-8, and sphere formation assays were used to detect cell proliferation and self-renewal, respectively. Wound healing and transwell assays were used to detect cell migration. Coimmunoprecipitation and molecular docking were used to determine the underlying mechanism through which SNORA51 regulates BCSCs-like properties. High SNORA51 expression was associated with a worse prognosis, overall survival, and disease-free survival, in 158 breast cancer patients and was also closely related to lymph node status, ER status, the Ki-67 index, histological grade, and TNM stage. Further analysis proved that SNORA51 could enhance and maintain stem cell-like properties, including cell proliferation, self-renewal, and migration, in breast cancer. Moreover, high SNORA51 expression could reduce nucleolar RPL3 expression, induce changes in the expression of NPM1 in the nucleolus and nucleoplasm, and ultimately increase c-MYC expression. Taken together, our findings demonstrated that SNORA51 could enhance BCSCs-like properties via the RPL3/NPM1/c-MYC pathway both in vitro and in vivo. Therefore, SNORA51 might be a significant biomarker and potential therapeutic target and might even provide a new viewpoint on the regulatory mechanism of snoRNAs in breast cancer or other malignant tumors.

Sulforaphane alleviates psoriasis by enhancing antioxidant defense through KEAP1-NRF2 Pathway activation and attenuating inflammatory signaling.

Psoriasis is a chronic inflammatory skin disease that affects millions of people worldwide. Sulforaphane (SFN) has been shown to have anti-inflammatory and antioxidant properties. In this study, we investigated the effects of SFN on a mouse model of psoriasis induced by imiquimod (IMQ) and its underlying molecular mechanism. Mice treated with SFN showed significant improvement in psoriatic symptoms, including reduced erythema, scales, and cutaneous thickness. Histopathological analysis and immunohistochemical staining revealed decreased expression of K16, K17, and Ki67 in SFN-treated mice, indicating reduced abnormal differentiation of keratinocytes and cutaneous inflammation. SFN treatment also reduced the activation of STAT3 and NF-κB pathways and downregulated pro-inflammatory cytokines IL-1ß, IL-6, and CCL2. In vitro experiments using HaCaT cells demonstrated that SFN inhibited IL-22 and TNF-α-induced activation of inflammatory pathways and keratinocyte proliferation. Network pharmacology analysis suggested that the KEAP1-NRF2 pathway might be involved in the protective effects of SFN on psoriasis. We observed reduced NRF2 expression in human psoriatic lesions, and subsequent experiments showed that SFN activated KEAP1-NRF2 pathway in vivo and in vitro. Importantly, NRF2-deficient mice exhibited aggravated psoriasis-like symptoms and reduced response to SFN treatment. Our findings indicate that SFN ameliorates psoriasis symptoms and inflammation through the KEAP1-NRF2 pathway, suggesting a potential therapeutic role for SFN in the treatment of psoriasis.

A signature of cuproptosis-related lncRNAs predicts prognosis and provides basis for future anti-tumor drug development in breast cancer.

Background: Breast cancer is the most prevalent malignancy worldwide and the leading culprit for women's death. Cuproptosis is a novel and promising modality of tumor cell death and the relationship with long non-coding RNAs (lncRNAs) remains shrouded in a veil. Studies in cuproptosis-related lncRNAs can aid in the clinical management of breast cancer and provide a basis for anti-tumor drug development. Methods: RNA-Seq data, somatic mutation data, and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Patients were divided into high- and low-risk groups according to the risk score. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to select prognostic lncRNAs to construct a risk score system. Its' prognostic value was confirmed in the training and validation cohorts subsequently. Functional analysis regarding cuproptosis-related lncRNAs was performed. Results: Eighteen cuproptosis-related lncRNAs were identified and 11 of them including AL023882.1, AC091588.1, AC138028.2, AC027514.1, AL592301.1, LRRC8C-DT, MFF-DT, NIFK-AS1, MECOM-AS1, OTUD6B-AS1 and RNF32-AS1 were selected for risk score system construction. The risk score was confirmed as an independent prognostic factor and patients in the high-risk group had a worse prognosis. A nomogram based on the independent prognostic factors was constructed for clinical decision aids. Further analyses revealed that patients in the high-risk group faced a heavier tumor mutational burden (TMB) and suppressed anti-tumor immunity. Besides, cuproptosis-related lncRNAs were associated with the expression of immune checkpoint inhibitors, N6-adenylate methylation (m6a), and drug sensitivity in breast cancer. Conclusions: A prognostic risk score system with satisfactory predictive accuracy was constructed. Besides, cuproptosis-related lncRNAs can influence the immune microenvironment, TMB, m6a, and drug sensitivity in breast cancer, which may provide a basis for future anti-tumor drug development.

Vitamin D attenuates DNCB-induced atopic dermatitis-like skin lesions by inhibiting immune response and restoring skin barrier function.

Atopic dermatitis (AD) is a common chronic inflammatory skin disease causing erythema and itching. The etiology of AD is complex and not yet clear. Vitamin D is a fat-soluble vitamin that promotes skin cell growth and differentiation and regulates immune function. This study aimed to explore the therapeutic effect of calcifediol, the active metabolite of vitamin D, on experimental AD and the possible mechanism of action. We found that the levels of vitamin D binding protein (VDBP) and vitamin D receptor (VDR) in biopsy skin samples from AD patients decreased compared with controls. We used 2,4-dinitrochlorobenzene (DNCB) to induce an AD mouse model on the ear and back of BALB/c mice. A total of five groups were used: the control group, the AD group, the AD + calcifediol group, the AD + dexamethasone group, and the calcifediol alone group. Under calcifediol treatment, mice exhibited reduced spinous layer thickening, reduced inflammatory cell infiltration, downregulated aquaporin 3 (AQP3) expression, and restored the barrier function of the skin. Simultaneous calcifediol treatment decreased STAT3 phosphorylation, inhibited inflammation and chemokine release, decreased AKT1 and mTOR phosphorylation, and suppressed epidermal cell proliferation and abnormal differentiation. In conclusion, our study demonstrated that calcifediol significantly protected mice against DNCB-induced AD. In a mouse model of AD, calcifediol may reduce inflammatory cell infiltration and chemokines by inhibiting the phosphorylation of STAT3 and may restore skin barrier function through the downregulation of AQP3 protein expression and inhibition of cell proliferation.

Identification of Energy Metabolism-Related Gene Signatures From scRNA-Seq Data to Predict the Prognosis of Liver Cancer Patients.

The increasingly common usage of single-cell sequencing in cancer research enables analysis of tumor development mechanisms from a wider range of perspectives. Metabolic disorders are closely associated with liver cancer development. In recent years, liver cancer has been evaluated from different perspectives and classified into different subtypes to improve targeted treatment strategies. Here, we performed an analysis of liver cancer from the perspective of energy metabolism based on single-cell sequencing data. Single-cell and bulk sequencing data of liver cancer patients were obtained from GEO and TCGA/ICGC databases, respectively. Using the Seurat R package and protocols such as consensus clustering analysis, genes associated with energy metabolism in liver cancer were identified and validated. An energy metabolism-related score (EM score) was established based on five identified genes. Finally, the sensitivity of patients in different scoring groups to different chemotherapeutic agents and immune checkpoint inhibitors was analyzed. Tumor cells from liver cancer patients were found to divide into nine clusters, with cluster 4 having the highest energy metabolism score. Based on the marker genes of this cluster and TCGA database data, the five most stable key genes (ADH4, AKR1B10, CEBPZOS, ENO1, and FOXN2) were identified as energy metabolism-related genes in liver cancer. In addition, drug sensitivity analysis showed that patients in the low EM score group were more sensitive to immune checkpoint inhibitors and chemotherapeutic agents AICAR, metformin, and methotrexate.

Alpha-7 nicotinic acetylcholine receptor agonist alleviates psoriasis-like inflammation through inhibition of the STAT3 and NF-κB signaling pathway.

Psoriasis is a chronic inflammatory cutaneous disease; it has been discovered that stimulation of the nervous system increases susceptibility to psoriasis. Although the cholinergic anti-inflammatory pathway, which is mediated by the alpha-7 nicotinic acetylcholine receptor (α7nAChR), is critical for controlling multiple types of inflammation, its expression pattern and pathogenesis function in psoriatic lesioned skin tissue are unknown. We hereby analyzed the expression of α7nAchR in human and mouse psoriatic skin tissue. In vivo, PNU-282987 or Methyllycaconitine, a specific agonist or antagonist of α7nAchR, were administered to imiquimod (IMQ)-induced psoriatic mouse models. The macroscopic appearance and histopathological features of the psoriatic mice skin were evaluated. In addition, cell proliferation and differentiation markers were investigated. The level of pro-inflammatory cytokines released from the lesioned skin, as well as the activation of the relevant signaling pathways, were measured. Our findings indicated that psoriatic lesional skin expressed an increased level of α7nAChR, with its tissue distribution being primarily in skin keratinocytes and macrophages. In an IMQ-induced murine psoriasis model, α7nAChR agonist PNU-282987 treatment alleviated psoriasis-like inflammation by down-regulating the expression of multiple types of pro-inflammatory mediators and normalized keratinocyte proliferation and differentiation, whereas α7nAChR antagonist treatment exacerbated its effect. Mechanically, we observed that activation of the α7nAChR inhibited the activation of the STAT3 and NF-κB signaling pathways in in vitro cultured HaCaT cells induced by Th17-related cytokine IL-6/IL-22 or Th1-related cytokine TNF-α. Taken together, these findings demonstrate that attenuation of psoriatic inflammation via the cholinergic anti-inflammatory pathway is dependent on α7nAChR activation.

Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy.

The recognition of denatured biological tissue is an indispensable part in the process of high intensity focused ultrasound treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the recognition of denatured biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity. The disadvantage will affect the recognition effect of denatured tissues. In order to solve the above problems, the method of multi-scale rescaled range permutation entropy (MRRPE) is proposed in this paper. The simulation results show that the MRRPE not only includes the amplitude information of the signal when calculating the signal complexity, but also extracts the extreme volatility characteristics of the signal effectively. The proposed method is applied to the HIFU echo signals during HIFU treatment, and the support vector machine (SVM) is used for recognition. The results show that compared with MPE and the multi-scale weighted permutation entropy (MWPE), the recognition rate of denatured biological tissue based on the MRRPE is higher, up to 96.57%, which can better recognize the non-denatured biological tissues and the denatured biological tissues.

Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer.

BACKGROUND: Recently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration. METHOD: The fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs. RESULTS: Five hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC. CONCLUSION: This study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Patients With Colon Cancer and to Assist in Immunotherapy.

BACKGROUND: Colon cancer is a malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divided it into different subtypes to facilitate individualized treatment. With the rise in the use of immunotherapy, its value in the field of tumor has begun to emerge. From the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored the same. METHODS: Cibersort algorithm was used to analyze the level of immune cell infiltration in patients with colon cancer in The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA), Consensus Clustering analysis, Lasso analysis, and univariate Kaplan-Meier analysis were used to screen and verify the hub genes associated with M2 macrophages. Principal component analysis (PCA) was used to establish the M2 macrophage-related score (M2I Score). The correlation between M2I Score and somatic cell variation and microsatellite instability (MSI) were analyzed. Furthermore, the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored. RESULTS: M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were identified as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in colon adenocarcinoma was determined based on four hub genes. The M2I Score was positively correlated with the tumor mutation load (TMB). The M2I Score of the group with high instability of microsatellites was higher than that of the group with low instability of microsatellites and microsatellite-stable group. Combined with the Cancer Immunome Atlas database, we concluded that patients with high M2I Scores were more sensitive to programmed cell death protein 1 (PD-1) inhibitors and PD-1 inhibitors combined with cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitors. The low-rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone. CONCLUSION: Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score. Patients were divided into two subgroups: high M2I Score group and low M2I Score group. TMB, MSI, and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

Impacts of bio-stimulants on pyrene degradation, prokaryotic community compositions, and functions.

Bio-stimulation of the indigenous microbial community is considered as an effective strategy for the bioremediation of polluted environments. This examination explored the near effects of various bio-stimulants on pyrene degradation, prokaryotic community compositions, and functions using 16S rRNA amplicon sequencing and qPCR. At first, the results displayed significant differences (p < 0.05) between the prokaryotic community structures of the control group, PYR (contains pyrene only), and bio-stimulants amended groups. Among the bio-stimulants, biochar, oxalic acid, salicylate, NPK, and ammonium sulfate augmented the pyrene degradation potential of microbial communities. Moreover, the higher abundance of genera, such as Flavobacterium, Hydrogenophaga, Mycobacterium, Rhodococcus, Flavihumibacter, Pseudomonas, Novosphingobium, etc., across the treatments indicated that these genera play a vital role in pyrene metabolism. Based on the higher abundance of GP-RHD and nidA genes, we speculated that Gram-positive prokaryotic communities are more competent in pyrene dissipation than Gram-negative. Furthermore, the marked abundance of nifH, and pqqC genes in the NPK and SA treatments, respectively, suggested that different bio-stimulants might enrich certain bacterial assemblages. Besides, the significant distinctions (p < 0.05) between the bacterial consortia of HA (humic acid) and SA (sodium acetate) groups from NPK, OX (oxalic acid), UR (urea), NH4, and SC (salicylate) groups also suggested that different bio-stimulants might induce distinct ecological impacts influencing the succession of prokaryotic communities in distinct directions. This work provides new insight into the bacterial degradation of pyrene using the bio-stimulation technique. It suggests that it is equally important to investigate the community structure and functions along with studying their impacts on degradation when devising a bio-stimulation technology.

Identification of Denatured Biological Tissues Based on Compressed Sensing and Improved Multiscale Dispersion Entropy during HIFU Treatment.

Identification of denatured biological tissue is crucial to high-intensity focused ultrasound (HIFU) treatment, which can monitor HIFU treatment and improve treatment efficiency. In this paper, a novel method based on compressed sensing (CS) and improved multiscale dispersion entropy (IMDE) is proposed to evaluate the complexity of ultrasonic scattered echo signals during HIFU treatment. In the analysis of CS, the method of orthogonal matching pursuit (OMP) is employed to reconstruct the denoised signal. CS-OMP can denoise the ultrasonic scattered echo signal effectively. Comparing with traditional multiscale dispersion entropy (MDE), IMDE improves the coarse-grained process in the multiscale analysis, which improves the stability of MDE. In the analysis of simulated signals, the entropy value of the IMDE method has less fluctuation compared with MDE, indicating that the IMDE method has better stability. In addition, MDE and IMDE are applied to the 300 cases of ultrasonic scattered echo signals after denoising (including 150 cases of normal tissues and 150 cases of denatured tissues). The experimental results show that the MDE and IMDE values of denatured tissues are higher than normal tissues. Both the MDE and IMDE method can be used to identify whether biological tissue is denatured. However, the multiscale entropy curve of IMDE is smoother and more stable than MDE. The interclass distance of IMDE is greater than MDE, and the intraclass distance of IMDE is less than MDE at different scale factors. This indicates that IMDE can better distinguish normal tissues and denatured tissues to obtain more accurate clinical diagnosis during HIFU treatment.

Identification of CTLA-4 associated with tumor microenvironment and competing interactions in triple negative breast cancer by co-expression network analysis.

Background: The study of CTLA-4 inhibitors has been one of the hot spots in the field of tumor immunotherapy. As the most immunogenic subtype of breast cancer, Triple negative breast cancer (TNBC) has a great potential in the treatment strategy. The aim of this study was to explore the relevant genes and pathways of CTLA-4 in TNBC and to explore the prognostic value, so as to provide a theoretical basis for clinical studies. Materials and methods: We used the data from The Cancer Genome Atlas (TCGA) to analyze the expression of CTLA-4 in different types of breast cancer, and analyzed the TNBC data of CTLA-4 related co-expression genes by WGCNA and enrichment analysis. LncRNA-miRNA-CTLA-4 network was constructed to explore the immune infiltration and immune checkpoint associated with CTLA-4. The effect of CTLA-4 on clinical outcomes in TNBC patients was also evaluated. Finally, we used data from GEO database to verify the differences of CTLA-4 in different molecular types of breast cancer and related prognostic results. Results: CTLA-4 was significantly higher in TNBC than in Luminal subtype and Her-2 + subtype (P=0.019 and P<0.001, separately), and was significantly higher in ER and PR negative samples than in ER and PR positive samples (P<0.001). CTLA-4 related genes mainly enriched in biological process of leukocyte differentiation, regulation of leukocyte activation and T cell activation. Hsa-mir-92a was found to be a survival significance marker associated with CTLA-4 and lncRNA-miRNA-CTLA-4 network was constructed. The results of immune infiltration analysis showed that CTLA-4 was mainly related with T cell (r=0.74). For immune checkpoints analysis, CTLA-4 was mainly related to PDCD1(r=0.72) and CD28(r=0.64). In TNBC, high expression of CTLA-4 is related to good survival (P=0.0061). Results consistent with previous analysis were obtained in the GEO database, the expression of CTLA-4 in TNBC was significantly higher than that in non-TNBC (p<0.001), CTLA-4 was associated with favorable survival of TNBC (p<0.001). Conclusion: Among all types of breast cancer, the expression of CTLA-4 was the highest in TNBC.CTLA-4 in TNBC can be regulated by hsa-mir-92a to form ceRNA networks and influence the prognosis of TNBC patients through the leukocyte differentiation, regulation of leukocyte activation and T cell activation pathway.

The Expression and Prognostic Significance of Claudin-8 and Androgen Receptor in Breast Cancer.

PURPOSE: Claudin-8 (CLDN8) has been identified as an androgen-regulated gene in prostate cancer. However, the role of CLDN8 has not been fully explored in breast cancer. We aimed to explore the expression of CLDN8 and androgen receptor (AR), determine the correlation between CLDN8 and AR, assess the prognostic value of CLDN8 and AR co-expression, and investigate the possible CLDN8 expression molecular mechanism in breast cancer. MATERIALS AND METHODS: Twenty-eight pairs of fresh tumor tissues and adjacent noncancerous tissues were evaluated by Western blot for CLDN8. Then, 142 breast cancer samples were determined by immunohistochemistry for CLDN8 and AR. The association of clinicopathological features with CLDN8, AR and CLDN8, and AR co-expression was examined. The Cancer Genome Atlas (TCGA) was used to demonstrate the expression of CLDN8 and correlation between CLDN8 and AR. Kaplan-Meier survival analysis was performed to assess the prognostic impact of CLDN8 and AR co-expression. The mechanisms related to CLDN8 expression in breast cancer were explored by Gene Set Enrichment Analysis (GSEA). RESULTS: CLDN8 was downregulated in breast cancer tissues and positively correlated with none lymph node metastasis (P=0.016), low histological grade (P=0.006), positive ER (P=0.014), positive PR (P=0.003), low Ki-67 index (P=0.017) and molecular subtypes (P=0.012). CLDN8 level was significantly associated with AR level (r=0.348; P<0.001). CLDN8 and AR co-expression was positively correlated with none lymph node metastasis (P=0.007), low histological grade (P=0.017), positive ER (P=0.019), positive PR (P=0.015) and low Ki-67 index group (P=0.038). CLDN8 and AR co-expression had a better clinical prognosis. CONCLUSION: The expression of CLDN8 is directly related to the expression of AR. CLDN8 and AR co-expression might be a potential prognostic evaluation factor for breast cancer patients.

Circular RNA hsa_circ_0000376 Participates in Tumorigenesis of Breast Cancer by Targeting miR-1285-3p.

This study was designed to identify novel circular RNAs and the related regulatory axis to provide research targets for the diagnosis and treatment of breast cancer. The circular RNA expression microarray "GSE101123" related to breast cancer was downloaded from the Gene Expression Omnibus database. The differentially expressed circular RNAs between tumor and normal samples were screened using Limma package. The targeted microRNAs of the differentially expressed circular RNAs and the targeted messenger RNAs of the microRNAs were predicted using miRanda and miRWalk, respectively, and a circular RNAs-microRNAs-messenger RNAs network was constructed. Then, functional enrichment analysis, protein-protein interaction network construction, and drug-gene interaction analysis were conducted for the messenger RNAs. A total of 11 differentially expressed circular RNAs were identified between the breast cancer and normal samples, of which 3 were upregulated, while 8 were downregulated. The circular RNA-microRNA-messenger RNA network contained 1 circular RNA (hsa_circ_0000376), 2 microRNAs (miR-1285-3p and miR-1286), and 353 messenger RNAs. The protein-protein interaction network contained 150 nodes and 240 interactions. The hub genes in the protein-protein interaction network were all targeted messenger RNAs of miR-1285-3p that were significantly enriched in the ubiquitin-proteasome system, apoptosis, cell cycle arrest-related pathways, and cancer-related pathways involving SMAD specific E3 ubiquitin protein ligase 1, ß-transducin repeat containing E3 ubiquitin protein ligase, tumor protein P53 among others. Twenty-two drugs were predicted to target 4 messenger RNAs, including tumor protein P53. A novel circular RNA, hsa_circ_0000376, was identified in breast cancer that may act as a sponge targeting miR-1285-3p expression which through its target genes, SMURF1, BTRC, and TP53, may further regulate tumorigenesis.