Comparing survival outcomes between neoadjuvant and adjuvant chemotherapy within T2N1M0 stage hormone receptor-positive, HER2-negative breast cancer: a retrospective cohort study based on SEER database.

BACKGROUND: Guideline recommendations for the application of neoadjuvant chemotherapy (NACT) in T2N1M0 stage hormone receptor-positive, HER2-negative (HR + /HER2-) breast cancer are ambiguous. The debate continues regarding whether NACT or adjuvant chemotherapy (ACT) offers superior survival outcomes for these patients. MATERIALS AND METHODS: Female patients diagnosed with HR + /HER2- breast cancer at T2N1M0 stage between 2010 and 2020, were identified from the Surveillance, Epidemiology, and End Results database and divided into two groups, the NACT group and the ACT group. Propensity score matching (PSM) was utilized to establish balanced cohorts between groups, considering baseline features. Kaplan-Meier (K-M) analysis and the Cox proportional hazards model were executed to assess the efficacy of both NACT and ACT in terms of overall survival (OS) and breast cancer-specific survival (BCSS). A logistic regression model was employed to examine the association between predictive variables and response to NACT. RESULTS: After PSM, 4,682 patients were finally included. K-M curves showed that patients receiving NACT exhibited significantly worse OS and BCSS when compared with patients undergoing ACT. Multivariable Cox analysis indicated that not achieving pathologic complete response (non-pCR) after NACT (versus ACT), was identified as an adverse prognostic factor for OS (HR 1.58, 95% CI 1.36-1.83) and BCSS (HR 1.70, 95% CI 1.44-2. 02). The logistic regression model revealed that low tumor grade independently predicted non-pCR. CONCLUSION: Among T2N1M0 stage HR + /HER2- patients, OS and BCSS of NACT were inferior to ACT. Patients who attained non-pCR after NACT demonstrated significantly worse survival outcomes compared with those who received ACT.

Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: A multicenter study.

BACKGROUND: Microsatellite instability (MSI) is associated with treatment response and prognosis in patients with rectal cancer (RC). However, intratumoral heterogeneity limits MSI testing in patients with RC. We developed a subregion radiomics model based on multiparametric magnetic resonance imaging (MRI) to preoperatively assess high-risk subregions with MSI and predict the MSI status of patients with RC. METHODS: This retrospective study included 475 patients (training cohort, 382; external test cohort, 93) with RC from two participating hospitals between April 2017 and June 2023. In the training cohort, subregion radiomic features were extracted from multiparametric MRI, which included T2-weighted, T1-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. MSI-related subregion radiomic features, classical radiomic features, and clinicoradiological variables were gathered to build five predictive models using logistic regression. Kaplan-Meier survival analysis was conducted to explore the prognostic information. RESULTS: Among the 475 patients (median age, 64 years [interquartile range, IQR: 55-70 years];304 men and 171 women), the prevalence of MSI was 11.16% (53/475). The subregion radiomics model outperformed the classical radiomics and clinicoradiological models in both training (area under the curve [AUC]=0.86, 0.72, and 0.59, respectively) and external test cohorts (AUC=0.83, 0.73, and 0.62, respectively). The subregion-clinicoradiological model combining clinicoradiological variables and subregion radiomic features performed the optimal, with AUCs of 0.87 and 0.85 in the training and external test cohorts, respectively. The 3-year disease-free survival rate of MSI groups predicted based on the model was higher than that of the predicted microsatellite stability (MSS) groups in both patient cohorts (training, P=0.032; external test, P=0.046). CONCLUSIONS: We developed and validated a model based on subregion radiomic features of multiparametric MRI to evaluate high-risk subregions with MSI and predict the MSI status of RC preoperatively, which may assist in individualized treatment decisions and positioning for biopsy.

Identification of a PANoptosis-related Gene Signature for Predicting the Prognosis, Tumor Microenvironment and Therapy Response in Breast Cancer.

Breast cancer (BC) is the most prevalent malignancy among women worldwide. Mounting evidence suggests that PANoptosis participates in cancer development and therapy. However, the role of PANoptosis in BC remains unclear. In this study, we identified ten PANoptosis-related genes using Cox regression analysis, random forest (RF) algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. A PANoptosis-related score (PRS) was calculated based on the coefficient of LASSO. Notably, we divided the patients into high- and low-risk groups according to the PRS and revealed a negative correlation between PRS and overall survival. Next, a nomogram model was constructed and validated to improve the clinical application of PRS. Functional enrichment analyses and the Bayesian network demonstrated that differentially expressed genes between high- and low-risk groups were mainly enriched in immune-related pathways. Besides, we found significant differences in tumor mutation burden and tumor immune microenvironment between patients in these two groups using bulk-RNA and single-cell RNA sequencing data. Furthermore, charged multivesicular body protein 2B (CHMP2B) was identified as the hub gene by combining LASSO, weighted gene co-expression network analysis, RF and eXtreme Gradient Boosting. Importantly, using immunohistochemistry analysis based on our tissue microarray, we found that CHMP2B was highly expressed in tumor tissue, and CD4 and CD8 were more likely to be positive in the CHMP2B-negative group. Survival analyses revealed that CHMP2B adversely impacted the survival of BC patients. In conclusion, we not only constructed a highly accurate predictive model based on PRS, but also revealed the importance of PANoptosis-related gene signature in the modulation of the tumor microenvironment and drug sensitivity in BC.

Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.

BACKGROUND: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. METHODS: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. RESULTS: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). CONCLUSIONS: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

BPIFB1 promotes metastasis of hormone receptor-positive breast cancer via inducing macrophage M2-like polarization.

Metastasis is an important factor affecting the prognosis of hormone receptor-positive breast cancer (BC). However, the molecular basis for migration and invasion of tumor cells remains poorly understood. Here, we identify that bactericidal/permeability-increasing-fold-containing family B member 1 (BPIFB1), which plays an important role in innate immunity, is significantly elevated in breast cancer and associated with lymph node metastasis. High expression of BPIFB1 and its coding mRNA are significantly associated with poor prognosis of hormone receptor-positive BC. Using enrichment analysis and constructing immune infiltration evaluation, we predict the potential ability of BPIFB1 to promote macrophage M2 polarization. Finally, we demonstrate that BPIFB1 promotes the metastasis of hormone receptor-positive BC by stimulating the M2-like polarization of macrophages via the establishment of BC tumor cells/THP1 co-culture system, qPCR, Transwell assay, and animal experiments. To our knowledge, this is the first report on the role of BPIFB1 as a tumor promoter by activating the macrophage M2 polarization in hormone receptor-positive breast carcinoma. Together, these results provide novel insights into the mechanism of BPIFB1 in BC.

Image quality and evaluation ability of magnetic resonance imaging techniques for thyroid-associated ophthalmopathy: Dixon fat-suppression technique vs. spectral attenuated inversion recovery.

Purpose: We aimed to compare two magnetic resonance imaging (MRI) techniques, Dixon and spectral attenuated inversion recovery (SPAIR) fat-suppression, in terms of image quality and suitability for evaluating thyroid-associated ophthalmopathy (TAO) lesion characteristics. Methods: This cross-sectional, retrospective study involved 70 patients with TAO (140 eyes) who underwent orbital coronal MRI examinations, including Dixon-transverse relaxation (T2)-weighted imaging (T2WI) and SPAIR-T2WI, between 2020 and 2022. We compared the fat-suppression quality and artifacts, noise (N), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR) of extraocular muscles (SIR-EOM) and lacrimal glands (SIR-LG), and TAO activity evaluation efficiency. Results: Dixon-T2WI showed a higher frequency of better subjective image quality and suitability for evaluating the characteristics of TAO lesions (65.7% vs. 14.3%) than SPAIR-T2WI. Fat-suppression quality and artifact scores were lower for Dixon-T2WI than for SPAIR-T2WI (p < 0.001). The N, SNR, and CNR values, EOM-SIR, and LG-SIR were higher for orbital coronal Dixon-T2WI than for SPAIR-T2WI (all p < 0.001). Clinical activity scores (CASs) showed positive correlations with SIR. The correlation between EOM-SIR and LG-SIR of orbital coronal Dixon-T2WI with CAS was higher than that of SPAIR-T2WI (0.590 vs. 0.493, all p < 0.001; 0.340 vs. 0.295, all p < 0.01). EOM-SIR and LG-SIR of Dixon-T2WI yielded a higher area under the curve than SPAIR-T2WI for evaluating TAO activity (0.865 vs. 0.760, p < 0.001; 0.695 vs. 0.617, p = 0.017). Conclusion: Dixon-T2WI yields higher image quality than SPAIR-T2WI. Furthermore, it has a stronger ability to evaluate TAO inflammation than SPAIR, with higher sensitivity and specificity in active TAO staging.

The added value of apparent diffusion coefficient and microcalcifications to the Kaiser score in the evaluation of BI-RADS 4 lesions.

PURPOSE: To explore the added value of combining microcalcifications or apparent diffusion coefficient (ADC) with the Kaiser score (KS) for diagnosing BI-RADS 4 lesions. METHODS: This retrospective study included 194 consecutive patients with 201 histologically verified BI-RADS 4 lesions. Two radiologists assigned the KS value to each lesion. Adding microcalcifications, ADC, or both these criteria to the KS yielded KS1, KS2, and KS3, respectively. The potential of all four scores to avoid unnecessary biopsies was assessed using the sensitivity and specificity. Diagnostic performance was evaluated by the area under the curve (AUC) and compared between KS and KS1. RESULTS: The sensitivity of KS, KS1, KS2, and KS3 ranged from 77.1% to 100.0%.KS1 yielded significantly higher sensitivity than other methods (P < 0.05), except for KS3 (P > 0.05), most of all, when assessing NME lesions. For mass lesions, the sensitivity of these four scores was comparable (p > 0.05). The specificity of KS, KS1, KS2, and KS3 ranged from 56.0% to 69.4%, with no statistically significant differences(P > 0.05), except between KS1 and KS2 (p < 0.05).The AUC of KS1 (0.877) was significantly higher than that of KS (0.837; P = 0.0005), particularly for assessing NME (0.847 vs 0.713; P < 0.0001). CONCLUSION: KS can stratify BI-RADS 4 lesions to avoid unnecessary biopsies. Adding microcalcifications, but not adding ADC, as an adjunct to KS improves diagnostic performance, particularly for NME lesions. ADC provides no additional diagnostic benefit to KS. Thus, only combining microcalcifications with KS is most conducive to clinical practice.

Pulmonary microbiota signatures adjacent to adenocarcinoma, squamous cell carcinoma and benign lesion.

Introduction: The occurrence and progression of lung cancer are influenced by pulmonary microbiota, yet the relationship between changes in the pulmonary microbiota and lung cancer remains unclear. Methods: To investigate the correlation between pulmonary microbiota and the signature of lung lesions, we analyzed the microbial composition at sites adjacent to the stage 1 adenocarcinoma, squamous carcinoma and benign lesion tissues in 49 patients by using 16S ribosomal RNA gene sequencing. We then conducted Linear discriminant analysis, receiver operating characteristic (ROC) curve analysis and PICRUSt prediction based on 16S sequencing results. Results: Overall, the microbiota composition at sites close to lung lesions showed significant differences between different lesion types. Based on the results of LEfSe analysis, Ralstonia, Acinetobacter and Microbacterium are the dominant genera of lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC) and benign lesions (BENL), respectively. Furthermore, we determined the diagnostic value of the abundance ratio of Ralstonia to Acinetobacter in adenocarcinoma patients through ROC curve analysis. The PICRUSt analysis revealed 15 remarkably different metabolic pathways in these lesion types. In LUAD patients, the increase of the pathway associated with xenobiotic biodegradation may be due to the continuous proliferation of microbe with degradation ability of xenobiotics, which implied that LUAD patients are often exposed to harmful environment. Discussion: The abundance of Ralstonia was related to the development of lung cancer. By measuring the abundance of microbiota in diseased tissues, we can distinguish between different types of lesions. The differences in pulmonary microbiota between lesion types are significant in understanding the occurrence and development of lung lesions.

Inhibition of glycolysis represses the growth and alleviates the endoplasmic reticulum stress of breast cancer cells by regulating TMTC3.

Considering the role of glycolysis inhibition as a novel therapeutic strategy for cancer, including breast cancer (BC), we wondered whether glycolysis could affect BC progression by regulating transmembrane O-mannosyltransferase-targeting cadherins 3 (TMTC3). Following the intervention, lactic acid production in BC cells was monitored, and viability, proliferation, and apoptosis assays were performed. The expressions of TMTC3 and endoplasmic reticulum (ER) stress- and apoptosis-related factors Caspase-12, C/EBP homologous protein (CHOP), glucose-regulated protein 78 (GRP78), B-cell lymphoma-2 (Bcl-2), and Bcl-2 associated X (Bax) were quantified. TMTC3 was lowly expressed in BC tissue and cell. The promotion of glycolysis via glucose represses TMTC3 expression and apoptosis yet enhances lactic acid production and growth of BC cell, along with promoted levels of Caspase-12, CHOP, GRP78, and Bcl-2 yet repressed level of Bax, while the contrary results were evidenced after 2-deoxyglycouse intervention. Overexpressed TMTC3 additionally abrogated the effects of glycolysis on increasing the viability and proliferation yet inhibiting the apoptosis of BC cells, with the increased expressions of Caspase-12, CHOP, and GRP78, and Bcl-2 yet decreased level of Bax. Collectively, inhibiting glycolysis restrained the growth and attenuated the ER stress of BC cell by regulating TMTC3.

Inhibition of lncRNA PCAT19 promotes breast cancer proliferation.

BACKGROUND: Breast cancer (BC) is the most common malignancy affecting women. It is vital to explore sensitive biological markers to diagnose and treat BC patients. Recent studies have proved that long noncoding RNAs (lncRNAs) were involved in breast tumor progression. Nonetheless, whether lncRNA prostate cancer-associated transcript 19 (PCAT19) impacts BC development remains unknown. METHODS: We performed various bioinformatic analyses, including machine learning models to identify critical regulatory lncRNAs affecting prognosis in BC. The in situ hybridization (ISH) assay was carried out to confirm the expression levels of lncRNA PCAT19 in tissue specimens. MTT assay, wound healing assay, and transwell assay were performed to investigate PCAT19's impact on proliferation, migration, and invasion of BC cells. Mouse xenografts were used to examine the proliferation-inhibiting function of PCAT19 in vivo. RESULTS: Among the prognosis-associated lncRNAs, PCAT19 predicted a favorable prognosis in BC. Patients with high expression levels of PCAT19 had a lower clinical stage and less lymph node metastasis. The PCAT19-related genes were enriched in signaling pathways involved in tumor development, indicating PCAT19 was an essential regulator of BC. Using the ISH assay, we confirmed the expression level of lncRNA PCAT19 in human BC tissues was lower than normal breast tissues. Moreover, the knockdown of PCAT19 further confirmed its inhibiting ability in BC cell proliferation. Correspondingly, overexpressing PCAT19 reduced tumor size in mouse xenografts. CONCLUSIONS: Our study demonstrated that lncRNA PCAT19 suppressed the development of BC. PCAT19 might be a promising prognostic biomarker, which provides new insights into risk stratification for BC patients.

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer.

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC).

Development and validation of a clinical predictive model for 1-year prognosis in coronary heart disease patients combine with acute heart failure.

Background: The risk factors for acute heart failure (AHF) vary, reducing the accuracy and convenience of AHF prediction. The most common causes of AHF are coronary heart disease (CHD). A short-term clinical predictive model is needed to predict the outcome of AHF, which can help guide early therapeutic intervention. This study aimed to develop a clinical predictive model for 1-year prognosis in CHD patients combined with AHF. Materials and methods: A retrospective analysis was performed on data of 692 patients CHD combined with AHF admitted between January 2020 and December 2020 at a single center. After systemic treatment, patients were discharged and followed up for 1-year for major adverse cardiovascular events (MACE). The clinical characteristics of all patients were collected. Patients were randomly divided into the training (n = 484) and validation cohort (n = 208). Step-wise regression using the Akaike information criterion was performed to select predictors associated with 1-year MACE prognosis. A clinical predictive model was constructed based on the selected predictors. The predictive performance and discriminative ability of the predictive model were determined using the area under the curve, calibration curve, and clinical usefulness. Results: On step-wise regression analysis of the training cohort, predictors for MACE of CHD patients combined with AHF were diabetes, NYHA ≥ 3, HF history, Hcy, Lp-PLA2, and NT-proBNP, which were incorporated into the predictive model. The AUC of the predictive model was 0.847 [95% confidence interval (CI): 0.811-0.882] in the training cohort and 0.839 (95% CI: 0.780-0.893) in the validation cohort. The calibration curve indicated good agreement between prediction by nomogram and actual observation. Decision curve analysis showed that the nomogram was clinically useful. Conclusion: The proposed clinical prediction model we have established is effective, which can accurately predict the occurrence of early MACE in CHD patients combined with AHF.

Nomogram development and validation to predict Ki-67 expression of hepatocellular carcinoma derived from Gd-EOB-DTPA-enhanced MRI combined with T1 mapping.

Objective: As an important biomarker to reflect tumor cell proliferation and tumor aggressiveness, Ki-67 is closely related to the high early recurrence rate and poor prognosis, and pretreatment evaluation of Ki-67 expression possibly provides a more accurate prognosis assessment and more better treatment plan. We aimed to develop a nomogram based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) combined with T1 mapping to predict Ki-67 expression in hepatocellular carcinoma (HCC). Methods: This two-center study retrospectively enrolled 148 consecutive patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI T1 mapping and surgically confirmed HCC from July 2019 to December 2020. The correlation between quantitative parameters from T1 mapping, ADC, and Ki-67 was explored. Three cohorts were constructed: a training cohort (n = 73) and an internal validation cohort (n = 31) from Shunde Hospital of Southern Medical University, and an external validation cohort (n = 44) from the Sixth Affiliated Hospital, South China University of Technology. The clinical variables and MRI qualitative and quantitative parameters associational with Ki-67 expression were analyzed by univariate and multivariate logistic regression analyses. A nomogram was developed based on these associated with Ki-67 expression in the training cohort and validated in the internal and external validation cohorts. Results: T1rt-Pre and T1rt-20min were strongly positively correlated with Ki-67 (r = 0.627, r = 0.607, P < 0.001); the apparent diffusion coefficient value was moderately negatively correlated with Ki-67 (r = -0.401, P < 0.001). Predictors of Ki-67 expression included in the nomogram were peritumoral enhancement, peritumoral hypointensity, T1rt-20min, and tumor margin, while arterial phase hyperenhancement (APHE) was not a significant predictor even included in the regression model. The nomograms achieved good concordance indices in predicting Ki-67 expression in the training and two validation cohorts (0.919, 0.925, 0.850), respectively. Conclusions: T1rt-Pre and T1rt-20min had a strong positive correlation with the Ki-67 expression in HCC, and Gd-EOB-DTPA enhanced MRI combined with T1 mapping-based nomogram effectively predicts high Ki-67 expression in HCC.

Survival Outcomes of Breast-Conserving Therapy versus Mastectomy in Early-Stage Breast Cancer, Including Centrally Located Breast Cancer: A SEER-Based Study.

Purpose: This study aims to analyze the survival outcomes of breast cancer (BC) patients, especially centrally located breast cancer (CLBC) patients undergoing breast-conserving therapy (BCT) or mastectomy. Methods: Surveillance, epidemiology, and end results (SEER) data of patients with T1-T2 invasive ductal or lobular breast cancer receiving BCT or mastectomy were reviewed. We used X-tile software to convert continuous variables to categorical variables. Chi-square tests were utilized to compare baseline information. The multivariate logistic regression model was performed to evaluate the relationship between predictive variables and treatment choice. Survival outcomes were visualized by Kaplan-Meier curves and cumulative incidence function curves and compared using multivariate analyses, including the Cox proportional hazards model and competing risks model. Propensity score matching was performed to alleviate the effects of baseline differences on survival outcomes. Result: A total of 180,495 patients were enrolled in this study. The breast preservation rates fluctuated around 60% from 2000 to 2015. Clinical features including invasive ductal carcinoma (IDC), lower histologic grade, smaller tumor size, fewer lymph node metastases, positive ER and PR status, and chemotherapy use were independently correlated with BCT in both BC and CLBC cohorts. In all the classic Cox models and competing risks models, BCT was an independent favorable prognostic factor for BC, including CLBC patients in most subgroups. In addition, despite the low breast-conserving rate compared with tumors located in the other areas, CLBC did not impair the prognosis of BCT patients. Conclusion: BCT is optional and preferable for most early-stage BC, including CLBC patients.

Predictive Value of Non-high-Density Lipoprotein Cholesterol and Neutrophil-Lymphocyte Ratio for Coronary Artery Vulnerable Plaques in Type 2 Diabetes Mellitus.

Background: Patients with diabetes have an increased risk of developing vulnerable plaques (VPs), in which dyslipidemia and chronic inflammation play important roles. Non-high-density lipoprotein cholesterol (non-HDL-C) and neutrophil-lymphocyte ratio (NLR) have emerged as potential markers of both coronary artery VPs and cardiovascular prognosis. This study aimed to investigate the predictive value of non-HDL-C and NLR for coronary artery VPs in patients with type 2 diabetes mellitus (T2DM). Methods: We retrospectively enrolled 204 patients with T2DM who underwent coronary computed tomography angiography between January 2018 and June 2020. Clinical data including age, sex, hypertension, smoking, total cholesterol, low-density lipoprotein cholesterol, HDL-C, triglyceride, non-HDL-C, glycated hemoglobin, neutrophil count, lymphocyte count, NLR, and platelet count were analyzed. Multivariate logistic regression was used to estimate the association between non-HDL-C, NLR, and coronary artery VPs. Receiver operating curve analysis was performed to evaluate the value of non-HDL-C, NLR, and their combination in predicting coronary artery VPs. Results: In our study, 67 patients (32.84%) were diagnosed with VPs, 75 (36.77%) with non-VP, and 62 (30.39%) with no plaque. Non-HDL-C and NLR were independent risk factors for coronary artery VPs in patients with T2DM. The areas under the ROC curve of non-HDL-C, NLR, and their combination were 0.748 [95% confidence interval (CI): 0.676-0.818], 0.729 (95% CI: 0.650-0.800), and 0.825 (95% CI: 0.757-0.887), respectively. Conclusion: Either non-HDL-C or NLR could be used as a predictor of coronary artery VPs in patients with T2DM, but the predictive efficiency and sensitivity of their combination would be better.

LncRNA MBNL1-AS1 Represses Proliferation and Cancer Stem-Like Properties of Breast Cancer through MBNL1-AS1/ZFP36/CENPA Axis.

Background: Emerging studies have revealed long noncoding RNAs (lncRNAs) were key regulators of cancer progression. In this research, the expression and roles of MBNL1-AS1 were explored in breast cancer (BC). Methods: In this study, the MBNL1-AS1 expression in breast cancer tissue, as well as in cell line, was studied by qRT-PCR assays. The effects of MBNL1-AS1 on proliferation and stemness were evaluated by MTT assays, colony formation assays, orthotopic breast tumor mice models, extreme limiting dilution analysis (ELDA), fluorescence in situ hybridization (FISH), flow cytometry assays, and sphere formation assays. Flexmap 3D assays were performed to show that MBNL1-AS1 downregulated the centromere protein A (CENPA) secretion in BC cells. Western blot, RNA pull-down assays, RNA immunoprecipitation (RIP) assays, and FISH were conducted to detect the mechanism. Results: The results showed that the expression levels of MBNL1-AS1 were downregulated in breast cancer tissues and cell lines. In vitro and in vivo studies demonstrated that overexpression of MBNL1-AS1 markedly inhibited BC cells proliferation and stemness. RNA pull-down assay, RIP assay, western blot assay, and qRT-PCR assay showed that MBNL1-AS1 downregulated CENPA mRNA via directly interacting with Zinc Finger Protein 36 (ZFP36) and subsequently decreased the stability of CENPA mRNA. Restoration assays also confirmed that MBNL1-AS1 suppressed the CENPA-mediated proliferation and stemness in breast cancer cells. Conclusions: The new mechanism of how MBNL1-AS1 regulates BC phenotype is elucidated, and the MBNL1-AS1/ZFP36/CENPA axis may be served as a therapeutic target for BC patients.

CircNR3C1 Alleviates Gastric Cancer Development by Inactivating AKT/mTOR.

Differential level and regulatory effect of circNR3C1 in gastric cancer (GC) were determined. The differential levels of circNR3C1 in clinical samples of GC were determined. The association of circNR3C1 level with pathological indicators of GC was analyzed. After intervening circNR3C1 levels in gastric cancer cells, proliferative and migratory changes were investigated. Furthermore, we measured AKT and mTOR protein levels in GC cells intervened by circNR3C1. Finally, the role of AKT/mTOR in GC cell phenotypes regulated by circNR3C1 was explored. circNR3C1 was markedly lowly expressed in GC cells and tissues. A low level of circNR3C1 predicted high incidences of lymphatic or distant metastasis of GC. Knockdown of circNR3C1 enhanced proliferation and migration abilities in BGC-823 cells, whereas overexpression of circNR3C1 yielded the opposite results in AGS cells. circNR3C1 downregulated mTOR and AKT in GC cells. In addition, induction of the AKT activator could reverse the attenuated proliferative and migratory potentials in GC cells overexpressing circNR3C1. On the contrary, induction of the AKT inhibitor reversed the stimulated malignant phenotypes of GC with circNR3C1 knockdown. circNR3C1 inhibits GC to proliferate and migrate by inactivating the AKT/mTOR signaling. It is also closely linked to GC metastasis.

Clinical information and management status of de novo stage IV breast cancer patients: a Chinese multicenter investigation (CSBrS-002).

BACKGROUND: Although de novo stage IV breast cancer is so far incurable, it has entered an era of individualized treatment and chronic disease management. Based on systemic treatment, whether the surgical resection of primary or metastatic foci of de novo stage IV breast cancer can bring survival benefits is currently controversial. We aimed to explore the clinicopathological factors and current status of the management of de novo stage IV breast cancer in China to provide a reference for clinical decisions. METHODS: Based on the assistance of Chinese Society of Breast Surgery, a retrospective study was conducted to analyze the clinical data of patients with de novo stage IV breast cancer in 33 centers from January 2017 to December 2018. The relationship between basic characteristic (age, menstrual status, family history, reproductive history, pathological type, estrogen receptor [ER] status, progesterone receptor [PR] status, human epidermal growth factor receptor 2 [HER2] status, Ki-67 percentage, and molecular subtype), and metastasis sites (lung metastasis, liver metastasis, and bone metastasis) was examined by Pearson Chi-square tests. RESULTS: A total of 468 patients with de novo stage IV breast cancer were enrolled. The median age of the enrolled patients was 51.5 years. The most common pathological type of primary lesion was invasive carcinoma (97.1%). Luminal A, luminal B, HER2 overexpressing, and triple-negative subtypes accounted for 14.3%, 51.8%, 22.1%, and 11.8% of all cases, respectively. Age, PR status, and HER2 status were correlated with lung metastasis (χ2 = 6.576, 4.117, and 8.643 and P = 0.037, 0.043, and 0.003, respectively). Pathological type, ER status, PR status, and molecular subtype were correlated with bone metastasis (χ2 = 5.117, 37.511, 5.224, and 11.603 and P = 0.024, <0.001, 0.022, and 0.009, respectively). Age, PR status, HER2 status, Ki-67 percentage, and molecular subtype were correlated with liver metastasis (χ2 = 11.153, 13.378, 10.692, 21.206, and 17.684 and P = 0.004, <0.001, 0.001, <0.001, and 0.001, respectively). Combined treatment with paclitaxel and anthracycline was the most common first-line chemotherapy regimen for patients with de novo stage IV breast cancer (51.7%). Overall, 91.5% of patients used paclitaxel-containing regimens. Moreover, 59.3% of hormone receptor-positive patients underwent endocrine therapy. CONCLUSIONS: In 2018, 1.07% of patients from all studied centers were diagnosed with de novo stage IV breast cancer. This study indicated that 95.1% of patients received systemic therapy and 54.2% of patients underwent surgical removal of the primary lesion in China.

IGF-1R Transported to the Cell Nuclei to Regulate the Proliferation of Breast Cancer Cells.

Under normal physiological conditions, IGF-1 (insulin-like growth factor-1) has important biological effects. However, many studies have found that IGF-1 is closely related to the occurrence and development of breast cancer. But up to now, the cellular properties of IGF-1 have not been systematically explored in breast cancer cell. It is well-known that the cellular properties and behaviors of IGF-1/IGF-1R are closely related to its biological functions. In the current study, we used the breast cancer cell line as a model to explore the biological characteristics of IGF-1/IGF-1R, and found that IGF-1/IGF-1R can be internalized into the cytoplasm. In addition, we also found that IGF-1R can also enter cell nuclei under the mediation of IGF-1. Further research found that the nuclear-localized IGF-1R has important potential biological effects, which is closely associated to the proliferation of breast cancer cell, this may be achieved by regulating IGF-1R-mediated intracellular signaling. The current research has laid the foundation for investigating the relationship between IGF-1/IGF-1R system and the occurrence and development of breast cancer.

Assessment of CPS + EG, Neo-Bioscore and Modified Neo-Bioscore in Breast Cancer Patients Treated With Preoperative Systemic Therapy: A Multicenter Cohort Study.

This study was to assess the prognosis stratification of the clinical-pathologic staging system incorporating estrogen receptor (ER)-negative disease, the nuclear grade 3 tumor pathology (CPS + EG), Neo-Bioscore, and a modified Neo-Bioscore system in breast cancer patients after preoperative systemic therapy (PST). A retrospective multicenter cohort study was conducted from 12 participating hospitals' databases from 2006 to 2015. Five-year disease free survival (DFS), disease specific survival (DSS), and overall survival (OS) were calculated using Kaplan-Meier Method. Area under the curve (AUC) of the three staging systems was compared. Wald test and maximum likelihood estimates in Cox proportional hazards model were used for multivariate analysis. A total of 1,077 patients were enrolled. The CPS + EG, Neo-Bioscore, and modified Neo-Bioscore could all stratify the DFS, DSS, and OS (all P < 0.001). While in the same stratum of Neo-Bioscore scores 2 and 3, the HER2-positive patients without trastuzumab therapy had much poorer DSS (P = 0.013 and P values < 0.01, respectively) as compared to HER2-positive patients with trastuzumab therapy and HER2-negative patients. Only the modified Neo-Bioscore had a significantly higher stratification of 5-year DSS than PS (AUC 0.79 vs. 0.65, P = 0.03). So, the modified Neo-Bioscore could circumvent the limitation of CPS + EG or Neo-Bioscore. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT03437837.