miR-183-5p Aggravates Breast Cancer Development via Mediation of RGS2

This study mainly explores how miR-183-5p pertains to breast cancer (BC) development. Functional assays were employed to test impacts of miR-183-5p in this cancer. Targeting between RGS2 and miR-183-5p was examined with dual-luciferase assay, and how their interaction pertains to cancer progression was further unraveled. miR-183-5p level was noticeably high in cancer tissue/cells. Overexpressing miR-183-5p could remarkably deteriorate cancer progression. The regulatory gene RGS2 levels was markedly low in BC, and two genes we researched were negatively correlated. It was uncovered by rescue assay that miR-183-5p/RGS2 axis mediated tumor-relevant behaviors in BC. Altogether, miR-183-5p aggravates BC development via mediation of RGS2. miR-183-5p supplies a promising target for BC therapy.

[Hepatic echinococcus granulosus: a clinicopathological analysis of thirteen cases]

Objective: To investigate the clinicopathologic characteristics of hepatic echinococcus granulosus (HEG). Methods: Thirteen cases of HEG were collected from Linzhi People's Hospital between January 2017 to October 2020, and their clinicopathologic features, ultrasound classification, immunophenotype and histochemical data were analyzed, retrospectively and the relevant literature was reviewed. Results: Thirteen patients (5 male patients, 8 female patients) were included in this cohort, and the mean age was 40 years. The most common clinical presentation was mild abdominal distention and pain (9/13). Based on WHO-IWGE ultrasound standardized classification, these cases were classified into 5 types, including type CL (1 case), type CE1 (2 cases), type CE2 (4 cases), type CE3 (3 cases) and type CE4 (3 cases). Gross examination revealed a solitary cyst localized in the liver, varying from 2.7 to 13.5 cm in diameter, and most of them(10/13)were more than 10 cm. Histopathologically, these cysts possessed a thin inner germinal layer and outer adventitial layer, and a central cavity filled with a clear"hydatid"fluid. The germinal layer was continuous and generated brood capsules and protoscoleces. The laminated membranes were clearly demonstrated by elastic fiber and Gomori's stains. Inside the"mother"cyst, there were a varying number of"daughter"vesicles of variable sizes. The inflammatory reaction around the cyst consisted of eosinophils, mononuclear cells immediately next to the cyst layer and sometimes formed granuloma and giant cells resembling the Langhan's type giant cells. The lymphoid cells were positive for CD20 and CD3. The CD68 immunohistochemistry clearly demonstrated epithelioid cells of granuloma in two cases. Moreover, immunohistochemistry revealed plasma cells were locally positive for CD38, IgG and IgG4, but not meeting the criteria for IgG4 related lesion. Conclusions: Hepatic echinococcus granulosus is a zoonotic parasitic disease prevalent in pastoral areas such as Tibet. It is important to understand its clinical features, ultrasound characteristics and histological morphology.

Changes of EGFR and SMC4 expressions in triple-negative breast cancer and their early diagnostic value

Background

To explore the diagnostic value of epidermal growth factor receptor (EGFR) and structural maintenance of chromosome protein 4 (SMC4) for triple-negative breast cancer.

Methods

A total of 213 breast cancer patients were selected and divided into triple-negative breast cancer (100 cases) and non-triple-negative breast cancer (113 cases) according to the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Patient information including age, body mass index (BMI), smoking history, drinking history, menopause, tumor classification, lymph node metastasis, distant metastasis, clinical stage, and EGFR and SMC4 expression were collected for all subjects. Logistic regression analysis was then used to evaluate the risk factors for triple-negative breast cancer. The ROC curve was also used to evaluate the clinical value of EGFR and SMC4 in the diagnosis of triple-negative breast cancer.

Results

Logistic regression analysis showed that high expression of SMC4 and high expression of EGFR were both risk factors for triple-negative breast cancer, with an odds ratio (OR) of 1.72 and 1.56, respectively (both P<0.05). ROC curve analysis results showed that the areas under the curve with high SMC4 expression and high EGFR expression for the diagnosis of triple-negative breast cancer were 0.84 and 0.78, respectively.

Conclusions

High expression of SMC4 and EGFR is significantly correlated with triple-negative breast cancer, and can be used as an auxiliary diagnostic indicator for triple-negative breast cancer.

Effect of breast-conserving surgery combined with sentinel lymph node biopsy and axillary preservation on the recurrence, metastasis, complications and cosmetic results of early breast cancer patients

Background

As one of the main malignant tumors, breast cancer remains a worldwide public health issue. Here, we aimed to analyze the effects of breast-conserving surgery (BCS) combined with sentinel lymph node biopsy (SLNB) and axillary preservation on the recurrence, metastasis, complications, and cosmetic results of early breast cancer patients (BCPs).

Methods

The clinical data of 143 BCPs admitted to our hospital from January 2017 to January 2019 were collected retrospectively, and all patients were female. Patients (76 cases) undergoing BCS combined with SLNB and axillary preservation treatment were set as the combined group, and 67 cases undergoing traditional modified radical surgery were set as the control group. After the perioperative conditions of patients in the two groups were compared, the patients were followed up for 14 months to record information on the quality of life, recurrence, metastasis, complications, and cosmetic results.

Results

The operation time, intraoperative blood loss, and extubation time of the combined group were significantly less than the control group (P<0.05); the quality of life of patients in both groups improved after treatment, but the quality of life of patients in the combined group was significantly higher than that of the control group (P<0.05); both groups of patients were followed up for 14 months after treatment, and there were no deaths. The rates of local recurrence and distant metastasis were 2.98% and 5.97% in the control group and 5.26% and 6.57% in the combined group, respectively, showing no significant difference between the two groups (P>0.05); the incidence of postoperative complications was 14.92% in the control group, which was significantly higher than the incidence of 3.94% of the combined group (P<0.05); the proportion of postoperative cosmetic results with an excellent and good rating was 59.7% in the control group, which was significantly lower than the 93.42% reported in the combined group (P<0.05).

Conclusions

BCS combined with SLNB and axillary preservation provided good clinical and cosmetic results and can improve the quality of life of patients and reduce the rate of recurrence and metastasis.

Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm

Abstract

Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm^-1, 2 854 cm^-1, 1 623 cm^-1, and 1 535 cm^-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm^-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.

[Correlation between polymorphism of CYP19A1, GSTM1, GSTT1 and GSTP1 gene and endometriosis]

Objective: To study the association between polymorphisms in CYP19A1 rs2899470, GSTM1, GSTT1, and GSTP1 rs1695 and the development of endometriosis. Methods: Between October 2015 and October 2017, 262 endometriosis patients and 275 control subjects were recruited from the Affiliated Hospital of Inner Mongolia Medical University. Genotyping was conducted using polymerase chain reaction-coupled with restriction fragment length polymorphism and multiplex allele specific polymerase chain reaction. Results: Individuals carrying the TT genotype of CYP19A1 rs2899470 expressed a higher risk of endometriosis than that carrying the GG genotype (P<0.01), and the adjusted OR was 2.33 (95%CI 1.27-4.33). In addition, individuals with the CYP19A1 rs2899470 the TT genotype aggravated the condition of endometriosis (OR=2.27, 95%CI 1.05-4.90). However, GSTM1, GSTT1 and GSTP1 rs1695 polymorphisms did not affect the pathogenesis of endometriosis. Conclusion: Our results suggested that CYP19A1 rs2899470 polymorphism is associated with the risk of endometriosis and the risk of disease.

SYCP2 expression is a novel prognostic biomarker in luminal A/B breast cancer

Aim: To explore the prognostic value of synaptonemal complex protein-2 (SYCP2) in different subtypes of breast cancer. Patients & materials: In silico bioinformatic analysis was conducted using large databases. Results: SYCP2 was only significantly upregulated in luminal B tumors compared with the adjacent normal tissues. SYCP2 expression was an independent indicator of shorter overall survival in luminal A patients (hazard ratio: 2.269; 95% CI: 1.059–4.862; p = 0.035) and luminal B patients (hazard ratio: 2.546; 95% CI: 1.020–6.355; p = 0.045). Its expression was negatively correlated with the methylation status of multiple CpG sites (cg22214414, cg23241473 and cg07347645) in both luminal A and luminal B tumors. Conclusion: Increased SYCP2 expression might be an independent indicator of shorter overall survival in both luminal A and luminal B patients.

[Effects of Temperature on FTIR Spectral Characteristics of Renal Tissue in Rats after Death]

OBJECTIVES

To analyse the Fourier transform infrared （FTIR） spectral data of renal tissue at different temperatures in rats after death, and to explore the effects of temperature on the FTIR spectral characteristics of renal tissue.

METHODS

The rats were sacrificed by cervical dislocation and placed at 4 ℃, 20 ℃ and 30 ℃. The FTIR spectral data of renal tissue were collected at different time points and analysed by data mining method.

RESULTS

The principal component analysis （PCA） results showed that there were significant trends of clustering in the samples of partial time point at 4 ℃, 20 ℃ and 30 ℃. Partial least square （PLS） regression models were established with the spectral data at three temperature groups. The performance of PLS regression models in 20 ℃ and 30 ℃ groups were more superior than that in 4 ℃ group, and the stability of the model in 20 ℃ group was better than that in 30 ℃ group.

CONCLUSIONS

There are differences in the FTIR spectral characteristics of renal tissue of rats after death at different temperatures. Temperature has a major impact on the performance of FTIR spectral PLS regression model. Therefore, in order to improve the accuracy of postmortem interval estimation, the effects of temperature on the model should be considered in the related study by spectral method.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method]

OBJECTIVES

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method.

METHODS

Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method.

RESULTS

The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （R²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h.

CONCLUSIONS

The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation.

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of P<0.05. Based on the protein‑protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several independent datasets. CDK2, CDKN1A, E2F1 and MYC were indicated as the potential feature genes in metastatic breast cancer.

Correlation between RNA Degradation Patterns of Rat's Brain and Early PMI at Different Temperatures

OBJECTIVES

To explore the correlation between early postmortem interval （PMI） and eight RNA markers of rat's brain at different temperatures.

METHODS

Total 222 SD rats were randomly divided into control group （PMI=0 h） and four experimental groups. And the rats in the experimental groups were sacrificed by cervical dislocation and respectively kept at 5 ℃, 15 ℃, 25 ℃ and 35 ℃ in a controlled environment chamber. The RNA was extracted from brain tissues, which was taken at 9 time points from 1 h to 24 h postmortem. The expression levels of eight markers, β-actin, GAPDH, RPS29, 18S rRNA, 5S rRNA, U6 snRNA, miRNA-9 and miRNA-125b, were detected using real-time fluorescent quantitative PCR, respectively. Proper internal reference was selected by geNorm software. Regression analysis of normalized RNA markers was performed by SPSS software. Mathematical model for PMI estimation was established using R software. Another 6 SD rats with known PMI were used to verify the mathematical model.

RESULTS

5S rRNA, miR-9 and miR-125b were suitable as internal reference markers for their stable expression. Both β-actin and GAPDH had well time-dependent degradation patterns and degraded continually with prolongation of PMI in 24 h postmortem. The mathematical model of the variation of ΔCt values with PMI and temperature was set up by R software and the model could be used for PMI estimation. The average error rates of model validation using β-actin and GAPDH were 14.1% and 22.2%, respectively.

CONCLUSIONS

The expression levels of β-actin and GAPDH are well correlated with PMI and environmental temperature. The mathematical model established in present study can provide references for estimating early PMI under various temperature conditions.

Studies on the growth metabolism of Bacillus thuringiensis and its vegetative insecticidal protein engineered strains by microcalorimetry

The metabolic power-times curves of Bacillus thuringiensis and its vegetative insecticidal protein engineered strains were determined at 30 degrees C by using a thermal activity monitor air Isothermal Microcalorimeter, ampoule method. From the power-times curves, the maximum power (Pmax) in the log phase, the growth rate constant (k), the generation times (tG), the time of the maximum power (tmax), the heat effects (Qlog) for log phase, and the total heat effect in 45 h (Qtotal) of B. thuringiensis strains can be obtained. The results indicate that their power-times curves are different. The relationship between their metabolic power-times curves and character of bacteria metabolism, and thermokinetics and gene expression were analyzed and discussed. The character of the bacteria power-times curves reflected the physiologic character of gene expression. The microcalorimetric method proved to be a reliable and sensitive tool for the assessment of the growth metabolism, the heat output in bacteria and its engineered strains. The determination of the thermokinetic character is beneficial to the control of fermentation.