Abstract Background and Purpose: With the aging of society, stroke has become a vital health problem for the middle-aged and elderly. Amounts of stroke's new risk factors have been found recently. It is necessary to develop a predictive risk stratification tool containing multi-dimensional risk factors for identifying high-risk people. Methods: The study included 5844 people (Age≥45) who participated in the China Health and Retirement Longitudinal Study, in 2011 and follow-up to 2018. Randomly divided the population into training and validation set by 1:1. Lasso Cox screened predictors for new-onset stroke. Developed a nomogram and stratified the population according to the score calculated in the nomogram through the X-tile program. Internal and external verification of nomogram was performed by ROC and calibration curves, and the Kaplan-Meier method was applied to identify the performance of the risk stratification system. Results: Lasso COX regression screened out 13 candidate predictors from 50 risk factors. Finally, nine predictors, including low physical performance, triglyceride-glucose index, etc., were included in the nomogram. The nomogram's overall performance was good in both internal and external validation (AUCs of three-year, five-year, seven-year in the training set was 0.71, 0.71, 0.71, and 0.67, 0.65, 0.66 in the validation set, respectively). The nomogram was proven to could excellently discriminate between low-, moderate-, and high-risk groups with the 7-year new-onset stroke of 3.36%, 8.32%, and 20.13%, respectively (P<0.001). Conclusions: This research developed a clinical predictive risk stratification tool that can effectively identify the different risks of new-onset stroke incidents in 7-years in the middle-aged and elderly Chinese.
•Establishing the first risk stratification nomogram for BC treated with NAC and validate its performance in BC cohorts.•Incorporating residual cancer burden index into predictive nomogram for the first time.•Predictive model can be utilized to predict DFS for all early-stage BC treated with NAC.•Performing a continuous rather than categorized model to predict individual survival.•The risk stratification can be used to select comparable population in trial design. BackgroundA favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients.Patients and methodsBetween January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort (n = 563) and an independent cohort (n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models.ResultsClinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively.ConclusionsIn patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials. A favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients. Between January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort (n = 563) and an independent cohort (n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models. Clinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively. In patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials.
Abstract Survival heterogeneity is observed among renal cell carcinoma (RCC) patients with metastases in different organs. Moreover, almost all previous prognostic nomograms based on data from metastatic RCC patients did not take competing events, such as death from cerebrovascular and heart diseases, into account. We aimed to construct novel prognostic nomograms for patients with lung metastatic clear cell RCC (LMCCRCC). Data of 712 non-Hispanic white LMCCRCC patients registered in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed. Nomograms for predicting overall survival (OS) and disease-specific survival (DSS) were established using the Cox approach and Fine and Gray approach, respectively, and their performances were assessed using the concordance index (C-index), calibration plots, and an independent cohort comprising 181 Hispanic patients. Sex, tumor grade, T stage, N stage, presence or absence of bone metastases, and presence or absence of brain metastases were independent predictors for both OS and DSS. Additionally, presence or absence of liver metastases was an independent predictor only for DSS. Meanwhile, age at diagnosis was independently associated with OS. The C-indexes of the nomograms were 0.702 for OS and 0.723 for DSS in internal validation. In external validation, the C-indexes were 0.700 for OS and 0.708 for DSS. Both internal and external calibration plots showed excellent consistency between the prediction and the observation. The current study developed a novel nomogram for predicting individual OS in LMCCRCC patients. Moreover, we constructed an effective competing risk nomogram for predicting their individual DSS for the first time.
Various clinical studies have determined that aspirin shows anticancer effects in many human malignant cancers, including human epidermal growth factor receptor-2 (HER-2)-positive breast cancer.However, the anti-tumor mechanism of aspirin has not been fully defined.The aim of this study was to determine the role of Compound C in enhancing the anticancer effect of aspirin.HER-2-positive breast cancer cell lines were treated with aspirin with or without Compound C pre-treatment; their phenotypes and mechanisms were then analyzed in vitro and in vivo.Aspirin exhibited anticancer effects in HER-2-positive breast cancer by inhibiting cell growth and inducing apoptosis through the activation of AMP-activated protein kinase (AMPK).Unexpectedly, pre-treatment with Compound C, a widely used AMPK inhibitor, induced robust anticancer effects in cells compared to aspirin monotherapy.This anticancer effect was not distinct in HER-2 negative breast cancer MDA-MB-231 cells and may be due to the inhibition of lipid metabolism mediated by c-myc.Besides, c-myc re-expression or palmitic acid supply could partially restored cell proliferation.Aspirin exhibits anticancer effects in HER-2-positive breast cancer by regulating lipid metabolism mediated by c-myc, and Compound C strengthens these effects in an AMPK-independent manner.Our results potentially provide a novel therapeutic strategy exploiting combined aspirin and Compound C therapy for HER-2-positive breast cancer, which acts by reducing de novo lipid synthesis.
In the tumor microenvironment, tumor-infiltrating immune cells (TIICs) are a key component. Different types of TIICs play distinct roles. CD8+ T cells and natural killer (NK) cells could secrete soluble factors to hinder tumor cell growth, whereas regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) release inhibitory factors to promote tumor growth and progression. In the meantime, a growing body of evidence illustrates that the balance between pro- and anti-tumor responses of TIICs is associated with the prognosis in the tumor microenvironment. Therefore, in order to boost anti-tumor response and improve the clinical outcome of tumor patients, a variety of anti-tumor strategies for targeting TIICs based on their respective functions have been developed and obtained good treatment benefits, including mainly immune checkpoint blockade (ICB), adoptive cell therapies (ACT), chimeric antigen receptor (CAR) T cells, and various monoclonal antibodies. In recent years, the tumor-specific features of immune cells are further investigated by various methods, such as using single-cell RNA sequencing (scRNA-seq), and the results indicate that these cells have diverse phenotypes in different types of tumors and emerge inconsistent therapeutic responses. Hence, we concluded the recent advances in tumor-infiltrating immune cells, including functions, prognostic values, and various immunotherapy strategies for each immune cell in different tumors.
Few longitude cohort studies investigated the risk of the duration of nighttime sleep and naps to the new-onset common chronic disease conditions (CDCs) in middle-aged (45-60) and the elderly (age ≥ 60) populations using an age-stratified strategy.The 7025 participants from The China Health and Retirement Longitudinal Study were screened as eligible subjects. Established 13 cohorts with CDCs, acquired their' sleep records in 2011, and obtained new-onset incidents of CDCs during follow-up in 2011-2018. Performed risk association analyses between sleep duration and 13 new-onset CDCs respectively.New-onset risk of four CDCs decreased with increasing nighttime sleep (p-nonlinear>0.05). The risk threshold was approximately 7 hours in middle-aged people and 6 hours in the elderly. For the middle-aged population, compared with 7-9hours sleep, <5hour and 5-7hours nighttime sleep were associated with 1.312∼1.675 times more risk of hypertension, kidney disease, diabetes or high blood sugar status, and multimorbidity; Compared with no nap, a 0-30 min nap was associated with 1.413(1.087∼1.837) times the heart disease risk. In the elderly, < 5 hours of night sleep was a significant risk factor for four CDCs including kidney disease and multimorbidity, etc. A long night's sleep (>9 hours) was connected with 61.2% reduction in risk of memory disease, a >90 min nap increased 62% risk of memory disease, and a 0-30 min nap was associated with higher risks of heart disease, hypertension, and a lower kidney disease risk.Nighttime sleep and daytime naps may have their own implications for the new-onset CDCs' risk in the aging process.
Abstract Purpose Breast cancer (BC) is the most prevalent malignant tumor worldwide among women, with the highest incidence rate. The mechanisms underlying nucleotide metabolism on biological functions in BC remain incompletely elucidated. Materials and Methods We harnessed differentially expressed nucleotide metabolism-related genes from The Cancer Genome Atlas-BRCA, constructing a prognostic risk model through univariate Cox regression and LASSO regression analyses. A validation set and the GSE7390 dataset were used to validate the risk model. Clinical relevance, survival and prognosis, immune infiltration, functional enrichment, and drug sensitivity analyses were conducted. Results Our findings identified four signature genes (DCTPP1, IFNG, SLC27A2, and MYH3) as nucleotide metabolism-related prognostic genes. Subsequently, patients were stratified into high- and low-risk groups, revealing the risk model's independence as a prognostic factor. Nomogram calibration underscored superior prediction accuracy. Gene Set Variation Analysis (GSVA) uncovered activated pathways in low-risk cohorts and mobilized pathways in high-risk cohorts. Distinctions in immune cells were noted between risk cohorts. Subsequent experiments validated that reducing SLC27A2 expression in BC cell lines or using the SLC27A2 inhibitor, Lipofermata, effectively inhibited tumor growth. Conclusions We pinpointed four nucleotide metabolism-related prognostic genes, demonstrating promising accuracy as a risk prediction tool for patients with BC. SLC27A2 appears to be a potential therapeutic target for BC among these genes.
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