Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast.

BACKGROUND: Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information. METHODS: The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system. RESULTS: LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests. CONCLUSIONS: For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.

Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma.

OBJECTIVES: This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS: This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted. RESULTS: The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization. CONCLUSION: A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage. ADVANCES IN KNOWLEDGE: This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.

Nomogram-Based Risk Stratification to Identify Patients with T3N0M0 Breast Cancer with Survival Benefit from Postmastectomy Radiotherapy.

BACKGROUND: The survival benefit of postmastectomy radiotherapy (PMRT) for patients with T3N0M0 breast cancer remains controversial. This study aimed to identify patients with a survival benefit from PMRT by developing a novel risk stratification model. PATIENTS AND METHODS: The study recruited 2062 patients with pT3N0M0 breast cancer from the Surveillance, Epidemiology, and End Results (SEER) database who underwent mastectomy between 2010 and 2019. Overall survival (OS) and breast-cancer-specific survival (BCSS) prognostic nomograms based on multivariate Cox regression were constructed to quantify the survival risk and classify patients into low- and high-risk groups. Subgroup analyses were undertaken to assess the role of PMRT according to age and risk stratification. RESULTS: In the overall cohort, PMRT was beneficial in improving OS in patients with pT3N0 breast cancer (5-year OS, non-PMRT versus PMRT: 76.6% vs. 84.2%, P < 0.001), while the benefit on BCSS was not significant (P = 0.084). On the basis of the risk stratification nomogram, in the high-risk group, PMRT improved OS in young patients by 10.1%, OS in elderly patients by 12.4%, and BCSS by 10.2% (P < 0.05), but the use of PMRT in the low-risk group did not improve OS and BCSS in all patients (P > 0.05). CONCLUSIONS: We presented a new method for quantifying risk using the nomogram to identify patients with high risk of pT3N0M0 breast cancer. This study found that older patients in the newly constructed high-risk group benefited from OS and BCSS benefits from PMRT, while for younger high-risk patients, there was only a benefit in terms of OS.

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

INTRODUCTION: Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. METHODS: Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. RESULTS: We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. CONCLUSION: We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC). METHODS: Three sets of ccRCC cohorts, ICGC_RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups. RESULTS: A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy. CONCLUSION: We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

Exploring oncogenes for renal clear cell carcinoma based on G protein-coupled receptor-associated genes.

G protein-coupled receptors (GPCRs) are a class of receptors on cell membranes that regulate various biological processes in cells, such as cell proliferation, differentiation, migration, apoptosis, and metabolism, by interacting with G proteins. However, the role of G protein-coupled receptors in predicting the prognosis of renal clear cell carcinoma is still unknown. The transcriptome data and clinical profiles of renal clear cell carcinoma patients, were downloaded from TCGA databases, and the validation group data were downloaded from number GSE167573, including 63 tumor samples and 14 normal samples. Single-cell RNA sequencing data were downloaded from the GEO database, No. GSE152938 and selected samples were used for GSEA enrichment analysis, WGCNA subgroup analysis, single-cell data analysis, and mutation analysis to explore the role of G protein-coupled receptor-related genes in the diagnosis and prognosis of renal clear cell carcinoma and to verify their reliability with cellular experiments. Finally, this study establishes a disease model based on G protein-coupled receptor-related genes, which may help to propose targeted therapeutic regimens in different strata of renal cell carcinoma patients.Author names: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author: Given name [Lisa Jia] Last name [Tran].It's ok!

Single-cell transcriptome analysis revealing the intratumoral heterogeneity of ccRCC and validation of MT2A in pathogenesis.

Clear-cell renal cell carcinoma (ccRCC) appears as the most common type of kidney cancer, the carcinogenesis of which has not been fully elucidated. Tumor heterogeneity plays a crucial role in cancer progression, which could be largely deciphered by the implement of scRNA-seq. The bulk and single-cell RNA expression profile is obtained from TCGA and study conducted by Young et al. We utilized UMAP, TSNE, and clustering algorithm Louvain for dimensionality reduction and FindAllMarkers function for determining the DEGs. Monocle2 was utilized to perform pseudo-time series analysis. SCENIC was implemented for transcription factor analysis of each cell subgroup. A series of WB, CFA, CCK-8, and EDU analysis was utilized for the validation of the role of MT2A in ccRCC carcinogenesis. We observed higher infiltration of T/NK and B cells in tumorous tissues, indicating the role of immune cells in ccRCC carcinogenesis. Transcription factor analysis revealed the activation of EOMES and ETS1 in CD8 + T cells, while CAFs were divided into myo-CAFs and i-CAFs, with i-CAFs showing distinct enrichment of ATF3, JUND, JUNB, EGR1, and XBP1. Through cell trajectory analysis, we discerned three distinct stages of cellular evolution, where State2 symbolizes normal renal tubular cells that underwent transitions into State1 and State3 as the CNV score ascended. Functional enrichment examination revealed an amplification of interferon gamma and inflammatory response pathways within tumor cells. The consensus clustering algorithm yielded two molecular subtypes, with cluster 2 being associated with advanced tumor stages and an abundance of infiltrated immune cells. We identified 17 prognostic genes through Cox and LASSO regression models and used them to construct a prognostic model, the efficacy of which was verified in multiple cohorts. Furthermore, we investigated the role of MT2A, one of our hub genes, in ccRCC carcinogenesis, and found it to regulate proliferation and migration of malignant cells. We depicted a detailed single-cell landscape of ccRCC, with special focus on CAFs, endothelial cells, and renal tubular cells. A prognostic model of high stability and accuracy was constructed based on the DEGs. MT2A was found to be actively implicated in ccRCC carcinogenesis, regulating proliferation and migration of the malignant cells.

Risk-dependent conditional survival analysis and annual hazard rate of inflammatory breast cancer.

PURPOSE: The real-time prognosis of patients with inflammatory breast cancer (IBC) after surviving for several years was unclear. We aimed to estimate survival over time in IBC using conditional survival (CS) and annual hazard functions. PATIENTS AND METHODS: This study recruited 679 patients diagnosed with IBC between 2010 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. We used the Kaplan-Meier method to estimate overall survival (OS). CS was the probability of surviving for another y years after surviving for x years after the diagnosis, and the annual hazard rate was the cumulative mortality rate of follow-up patients. Cox regression analyses were used to identify prognostic factors, and changes in real-time survival and immediate mortality in surviving patients were assessed within these prognostic factors. RESULTS: CS analysis showed real-time improvement in survival, with 5-year OS updated annually from the initial 43.5% to 52.2%, 65.3%, 78.5%, and 89.0% (surviving 1-4 years, respectively). However, this improvement was relatively small in the first two years after diagnosis, and the smoothed annual hazard rate curve showed increasing mortality during this period. Cox regression identified seven unfavorable factors at diagnosis, but only distant metastases remained after five years of survival. Analysis of the annual hazard rate curves showed that mortality continued to decrease for most survivors, except for metastatic IBC. CONCLUSION: Real-time survival of IBC improved dynamically over time, and the magnitude of this improvement was non-linear, depending on survival time and clinicopathological characteristics.

Effects of Probiotic Supplementation on Nutrient Intake, Ghrelin, and Adiponectin Concentrations in Diabetic Hemodialysis Patients.

Objective: The paper aimed to explore the effect of probiotic supplementation on nutrient intake, Ghrelin, and adiponectin concentrations in diabetic hemodialysis patients. Methods: A total of 86 patients with diabetic nephropathy who received hemodialysis treatment in the Department of Nephrology of the First People's Hospital of Shanghai from May 2019 to March 2021 were selected as the research subjects, including 52 male patients and 34 female patients, with an average age of 56.57 ± 4.28. According to the research protocol, the patients were divided into the control group (n = 30) and the observation group (n = 56). In the control group, dietary soybean milk was used as a placebo. In the observation group, capsules containing probiotics Lactobacillus acidophilus, Lactobacillus casei, and Bifidobacterium were taken with soybean milk. All patients signed an informed consent form before being included in the study. The results of the experimental biochemical analysis and the archived data counted the general data of the patients. Plasma adiponectin concentrations were measured with a commercially available human enzyme immunoassay kit. Ghrelin concentrations were estimated by specific commercial methods. Correlation software was used to calculate patient nutritional intake data. Serum creatinine, insulin resistance, fasting blood glucose, and levels of oxidative stress and inflammatory factors were measured using appropriate biochemical assays. Results: There was no difference in baseline characteristics between the two groups (P > .05). Before treatment, there was no difference in serum adiponectin concentration between the two groups (P > .05). After treatment, the serum adiponectin concentration in the observation group was lower than in the control group (P < .05). Before treatment, there was no difference in serum ghrelin levels between the two groups (P > .05). After treatment, serum ghrelin levels in the observation group were higher than in the control group (P < .05). Before treatment, there was no difference in nutrient intake between the two groups (P > .05). After treatment, the nutrient intake in the observation group was higher than in the control group (P < .05). Serum creatinine, fasting blood glucose, urine protein/creatinine ratio, and HOMA-IR in the observation group were lower than in the control group (P < .05). The serum levels of malondialdehyde, C-reactive protein, and TNF-α in the observation group were lower than those in the control group (P < .05), and the levels of glutathione in the observation group were higher than those in the control group (P < .05). Conclusion: Supplementation of probiotics in DN dialysis patients can increase serum ghrelin concentration, increase nutrient intake through appetite regulation, and reduce adiponectin level, which is beneficial to blood sugar control, insulin resistance, and renal function.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic triple-negative breast cancer.

Background: Conditional survival (CS) is defined as the possibility of further survival after patients have survived for several years since diagnosis. This may be highly valuable for real-time prognostic monitoring, especially when considering individualized factors. Such prediction tools were lacking for non-metastatic triple-negative breast cancer (TNBC). Therefore, this study estimated CS and developed a novel CS-nomogram for real-time prediction of 10-year survival. Methods: We recruited 32,836 non-metastatic TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019), who were divided into training and validation groups according to a 7:3 ratio. The Kaplan-Meier method estimated overall survival (OS), and the CS was calculated using the formula CS(y|x) =OS(y+x)/OS(x), where OS(x) and OS(y+x) were the survival of x- and (x+y)-years, respectively. The least absolute shrinkage and selection operator (LASSO) regression identified predictors to develop the CS-nomogram. Results: CS analysis reported gradual improvement in real-time survival over time since diagnosis, with 10-year OS updated annually from an initial 69.9% to 72.8%, 78.1%, 83.0%, 87.0%, 90.3%, 93.0%, 95.0%, 97.0%, and 98.9% (after 1-9 years of survival, respectively). The LASSO regression identified age, marriage, race, T status, N status, chemotherapy, surgery, and radiotherapy as predictors of CS-nomogram development. This model had a satisfactory predictive performance with a stable 10-year time-dependent area under the curves (AUCs) between 0.75 and 0.86. Conclusions: Survival of non-metastatic TNBC survivors improved dynamically and non-linearly with survival time. The study developed a CS-nomogram that provided more accurate prognostic data than traditional nomograms, aiding clinical decision-making and reducing patient anxiety.

Association of Androgen-Receptor Gene Mutations with the Copy Number of Androgen-Receptor Silk Protein A Complex and Glutathione-S-Transferases T1 and M1 in Prostate Cancer Patients.

Objective: The purpose of our work was to explore the association of mutations in the androgen receptor gene and copy numbers of the androgen-receptor silk protein A complex with glutathione-S-transferases T1 and M1 in prostate cancer patients. Materials and Methods: Eighty-five patients with PC and 85 healthy controls were included in the study. Fasting peripheral venous blood was collected, whole blood genomic DNA was extracted, and AR gene-receptor genotype was detected by a high-resolution melting curve analysis detection technology. Expression levels of androgen receptor (AR) and filamin protein A (FlnA) were detected by Western blotting. RT-PCR was used to detect the copy number of T1 and M1 glutathione-S-transferases. Results: The wild-type androgen receptor gene rs5918762 is of TT type. The frequencies of CC and TC genes in the prostate cancer group were significantly higher than those in the normal control group (P < 0.05). Compared with TT-type PC patients, PC patients with TC-type and CC-type had higher expression levels of sex hormone receptor silk protein A complex and higher copy numbers of GSTT1 and GSTM1 (P < 0.05). Androgen-receptor gene mutation (T ⟶ C) was significantly positively correlated with the expression level of androgen-receptor silk protein A complex and the copy number of GSTT1 and GSTM1. Conclusion: Androgen-receptor gene polymorphisms were significantly associated with expression levels of androgen receptor complex A and silk proteins, and copy numbers of T1 and M1 glutathione-S-transferases. A combination of four factors can be used to identify prostate cancer susceptibility and disease progression.

Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation.

Background: Locally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time. Methods: LABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan-Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model. Results: CS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1-6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients. Conclusions: The real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.

Development of a nomogram for predicting grade 2 or higher acute hematologic toxicity of cervical cancer after the pelvic bone marrow sparing radiotherapy.

Background: Acute hematologic toxicity (HT) is a common complication during radiotherapy of cervical cancer which may lead to treatment delay or interruption. Despite the use of intensity-modulated radiation therapy (IMRT) with the pelvic bone marrow (PBM) sparing, some patients still suffer from acute HT. We aimed to identify predictors associated with HT and develop a nomogram for predicting grade 2 or higher (G2+) acute HT in cervical cancer following the PBM sparing strategy. Methods: This study retrospectively analyzed 125 patients with cervical cancer who underwent IMRT with the PBM sparing strategy at our institution. Univariate and multivariate logistic regression, best subset regression, and least absolute shrinkage and selection operator (LASSO) regression, respectively, were used for predictor screening, and Akaike information criterion (AIC) was used to determine the best model for developing the nomogram. Finally, we quantified the risk of G2+ acute HT based on this model to establish a risk stratification. Results: The independent predictors used to develop the nomogram were histological grade, pre-radiotherapy chemotherapy, pre-radiotherapy HT, and radiotherapy [IMRT alone vs. concurrent chemoradiotherapy (CCRT)] which were determined by the univariate and multivariate logistic regression with the minimum AIC of 125.49. Meanwhile, the heat map showed that there is no multicollinearity among the predictors. The nomogram was well-calibrated to reality, with a Brier score of 0.15. The AUC value was 0.82, and the median Brier score and AUC in 1000 five-fold cross-validation were 0.16 and 0.80, respectively. The web version developed together was very easy to use. The risk stratification indicated that high-risk patients (risk point > 195.67) were more likely to develop G2+ acute HT [odds ratio (OR) = 2.17, 95% confidence interval (CI): 1.30-3.05]. Conclusion: This nomogram well-predicted the risk of G2+ acute HT during IMRT in cervical cancer after the PBM sparing strategy, and the constructed risk stratification could assist physicians in screening high-risk patients and provide a useful reference for future prevention and treatment strategies for acute HT.

Development and Validation a Survival Prediction Model and a Risk Stratification for Elderly Locally Advanced Breast Cancer.

PURPOSE: we aimed to develop an individualized survival prediction model for elderly locally advanced breast cancer (LABC) and stratify its risk to assist in the treatment and follow-up of patients. METHODS: Elderly LABC data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The best model was screened using Cox, least absolute shrinkage and selection operator (LASSO) and best subset regression to construct the nomogram. After internal and external validation of this model, risk stratification was established, and differences between risk groups were assessed using Kaplan-Meier method. RESULTS: A total of 10,697 elderly LABC patients were divided into a training group (n = 7131) and a validation group (n = 3566) with a 5-year overall survival rate of 57.6% [confidence interval (CI): 56.4%-58.7%]. A nomogram was developed using age, marital status, histological grading, estrogen and progesterone receptors, surgery, radiation therapy, and chemotherapy as predictors. This model was evaluated and validated to perform well, with a discrimination index of 0.744 (95% CI: 0.734-0.753). Patients were divided into low, medium and high groups based on risk scores, and there was a significant difference in survival between the 3 groups. CONCLUSION: The prognosis of elderly LABC was poor. The nomogram constructed based on prognostic factors could accurately predict the prognosis, which would provide a reference for treatment and follow-up.

Effects of etoposide combined with cisplatin on prognosis of patients with castration-resistant prostate cancer who failed castration treatment.

OBJECTIVE: To determine the influences of etoposide combined with cisplatin on prognosis of patients with castration-resistant prostate cancer (CRPC) who failed castration treatment. METHODS: A total of 100 patients with metastatic CRPC who failed castration treatment in our hospital from January 2015 to January 2017 were retrospectively analyzed. The patients were divided into a control group (n=59) treated with docetaxel combined with prednisone and an experimental group (n=41) treated with etoposide combined with cisplatin (EP). The change in prostate-specific antigen (PSA) level was adopted as the evaluation criterion for efficacy, by which the total clinical effective rate of patients was calculated. The neurologic rating scale (NRS) was adopted to evaluate the pain of patients, and the incidence of adverse reactions was compared between the two groups. Cox regression was carried out to analyze independent prognostic factors impacting 3-year survival. RESULTS: The experimental group showed a significantly better clinical improvement than the control group (P<0.05). According to further analysis, the experimental group had a significantly higher clinical efficacy rate than the control group (P<0.05). Life quality scores of the experimental group were higher than those of the control group (all P<0.05). The two groups were not greatly different in bone pain, or incidence of adverse reactions (both P>0.05). The median survival time of the control group was 15.9 months, while that of the experimental group was 18 months, and the control group experienced a greatly shorter median survival time than the experimental group (P=0.040). According to Cox regression analysis, Gleason score, clinical stage, and metastasis were independent factors impacting the patients' 3-year prognosis (all P<0.05). CONCLUSION: EP regimen can strongly improve the 3-year survival rate of patients, without increasing adverse reactions.

Long non-coding RNA MAGI2-AS3 inactivates STAT3 pathway to inhibit prostate cancer cell proliferation via acting as a microRNA-424-5p sponge.

Aberrant expression of long non-coding RNAs (lncRNAs) that results in sustained activation of cell growth promoting pathways is an important mechanism in driving prostate cancer progression. In the present study, we explored differentially expressed lncRNAs in two microarray datasets of prostate benign and malignant tissues. We found that MAGI2-AS3 was one of the most downregulated lncRNAs in prostate tumors, which was further confirmed in our collected clinical samples. The function assays showed that MAGI2-AS3 overexpression decreased cell viability and led to obvious cell apoptosis in PC-3 and DU145 prostate cancer cells. Elevation of MAGI2-AS3 decreased the activity of STAT3 in PC-3 and DU145. In addition, microRNA-424-5p (miR-424-5p), a positive regulator of STAT3 pathway, was predicted as a target of MAGI2-AS3, furthermore, the interaction between MAGI2-AS3 and miR-424-5p was confirmed via reverse-transcript polymerase chain reaction (RT-qPCR), dual luciferase reporter assay and RNA immunoprecipitation (RIP). MAGI2-AS3 upregulated miR-424-5p and downregulated COP1 in PC-3 and DU145. More importantly, IL6-induced activation of STAT3 pathway could attenuate the biological effect of MAGI2-AS3 in PC-3 and DU145. In clinical samples, MAGI2-AS3 levels were negatively correlated with miR-424-5p expression, while positively correlated with COP1 mRNA expression. Altogether, the current study revealed MAGI2-AS3 as a novel negative regulator of prostate cancer development.

Conditional survival analysis and real-time prognosis prediction for cervical cancer patients below the age of 65 years.

Background: Survival prediction for cervical cancer is usually based on its stage at diagnosis or a multivariate nomogram. However, few studies cared whether long-term survival improved after they survived for several years. Meanwhile, traditional survival analysis could not calculate this dynamic outcome. We aimed to assess the improvement of survival over time using conditional survival (CS) analysis and developed a novel conditional survival nomogram (CS-nomogram) to provide individualized and real-time prognostic information. Methods: Cervical cancer patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method estimated cancer-specific survival (CSS) and calculated the conditional CSS (C-CSS) at year y+x after giving x years of survival based on the formula C-CSS(y|x) =CSS(y+x)/CSS(x). y indicated the number of years of further survival under the condition that the patient was determined to have survived for x years. The study identified predictors by the least absolute shrinkage and selection operator (LASSO) regression and used multivariate Cox regression to demonstrate these predictors' effect on CSS and to develop a nomogram. Finally, the CSS possibilities predicted by the nomogram were brought into the C-CSS formula to create the CS-nomogram. Results: A total of 18,511 patients aged <65 years with cervical cancer from 2004 to 2019 were included in this study. CS analysis revealed that the 15-year CSS increased year by year from the initial 72.6% to 77.8%, 84.5%, 88.8%, 91.5%, 93.5%, 94.8%, 95.7%, 96.4%, 97.3%, 98.0%, 98.5%, 99.1%, and 99.4% (after surviving for 1-13 years, respectively), and found that when survival exceeded 5-6 years, the risk of death from cervical cancer would be less than 5% in 10-15 years. The CS-nomogram constructed using tumor size, lymph node status, distant metastasis status, and histological grade showed strong predictive performance with a concordance index (C-index) of 0.805 and a stable area under the curve (AUC) between 0.795 and 0.816 over 15 years. Conclusions: CS analysis in this study revealed the gradual improvement of CSS over time in long-term survived cervical cancer patients. We applied CS to the nomogram and developed a CS-nomogram successfully predicting individualized and real-time prognosis.

Nomogram Predicting the Risk of Locoregional Recurrence After Mastectomy for Invasive Micropapillary Carcinoma of the Breast.

BACKGROUND: The risk of locoregional recurrence (LRR) after mastectomy for breast invasive micropapillary carcinoma (IMPC) remains poorly defined. We aimed to construct an effective prognostic nomogram to estimate the individualized risk of LRR for providing accurate information for long-term follow-up. PATIENTS AND METHODS: A total of 388 patients with breast IMPC were included in the current study. Based on the Cox regression and clinical significance, a nomogram with an online prediction version was created. This model was evaluated and internally validated by concordance index and calibration plot. Receiver operating characteristic curve and decision curve analysis were used to assess the discrimination and clinical utility, and Kaplan-Meier curves estimated the probability of LRR. RESULTS: The variables (age, lymph node metastasis, hormone receptor status, lymphovascular invasion, histologic grade, and adjuvant radiotherapy) were included in the nomogram. This model was well-calibrated to predict the possibility of LRR and displayed favorable clinical utility; the concordance index was 0.86 (95% confidence interval, 0.81-0.91), which was higher than any single predictor. The area under the curve of the nomogram was 0.89, whereas that of the conventional staging system was 0.72. An online prognostic nomogram was built for convenient use. Kaplan-Meier curves showed that the nomogram had a better risk stratification than the conventional staging system. CONCLUSIONS: The nomogram could accurately predict the individualized risk of LRR after mastectomy for breast IMPC. By identifying the risk stratification, this model is expected to assist clinicians and patients in improving long-term follow-up strategies.

Analysis on coupled vibration of piezoelectric ceramic stack with two piezoelectric ceramic elements.

A longitudinally polarized piezoelectric ceramic stack with two piezoelectric ceramic elements is an important part of sandwich piezoelectric transducers. The three-dimensional coupled vibration of piezoelectric ceramic stack is analyzed. Since the longitudinal and radial dimensions of particular piezoelectric ceramic stacks are approximately equal, one-dimensional theory cannot be used for analysis. The piezoelectric ceramic stack is analyzed by the equivalent elastic method, which is an approximate analytical method, and it is considered that the coupled vibration of a piezoelectric ceramic stack is composed of equivalent radial and longitudinal vibrations. These two equivalent vibrations are connected by a mechanical coupling coefficient. The radial and longitudinal electromechanical equivalent circuits are obtained, and the resonance frequency equations are derived. The dependency of the radial and longitudinal resonance frequencies on the geometrical dimensions for piezoelectric ceramic stack is analyzed. The height or radius has a large influence on the longitudinal resonance frequency or the radial resonance frequency. Two sets of piezoelectric ceramic stacks are fabricated. The experimental results, COMSOL simulated results, and theoretical results are in good agreement.