As the malignancy with the highest global incidence, breast cancer represents a significant threat to women’s health. Recent advances have shed light on the importance of mitochondrial function in cancer, particularly in metabolic reprogramming within tumors. Recognizing this, we developed a novel risk signature based on mitochondrial-related genes to improve prognosis prediction and risk stratification in breast cancer patients. In this study, transcriptome data and clinical features of breast cancer samples were extracted from two sources: the TCGA, serving as the training set, and the METABRIC, used as the independent validation set. We developed the signature using LASSO-Cox regression and assessed its prognostic efficacy via ROC curves. Furthermore, the signature was integrated with clinical features to create a Nomogram model, whose accuracy was validated through clinical calibration curves and decision curve analysis. To further elucidate prognostic variations between high and low-risk groups, we conducted functional enrichment and immune infiltration analyses. Additionally, the study encompassed a comparison of mutation landscapes and drug sensitivity, providing a comprehensive understanding of the differing characteristics in these groups. Conclusively, we established a risk signature comprising 8 mitochondrial-related genes—ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3, and BCL2A1. This signature was identified as an independent risk predictor for breast cancer patient survival, exhibiting a significant high hazard ratio (HR = 3.028, 95%CI 2.038–4.499, P < 0.001). Patients in the low-risk group showed a more favorable prognosis, with enhanced immune infiltration, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs. In contrast, the high-risk group exhibited an adverse trend in these aspects. This risk signature represents a novel and effective prognostic indicator, suggesting valuable insights for patient stratification in breast cancer.

Background: Screening-based and risk-based strategies are the 2 strategies for preventing group B streptococcus (GBS) diseases in neonates. We aimed to compare the effects of these 2 strategies in reducing the incidence of early-onset GBS sepsis (GBS-EOS) and their effects on the incidence of non-GBS sepsis. Methods: PubMed, Embase, Web of Science and The Cochrane Central Register of Controlled Trials were searched for the period from January 1, 1996, to December 31, 2018. Randomized controlled trials and cohort studies that compared the effects of risk-based and screening-based strategies were eligible for the meta-analysis. The I ² statistic was used for assessing the statistical heterogeneity across studies. Pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs) were calculated using the random effects model. Results: There were 18 cohort studies comparing the incidence of GBS-EOS between the 2 strategies, involving a total of 604,869 newborns and 791 GBS-EOS cases. The heterogeneity across studies was moderate (I ² = 45%), and the pooled analysis yielded a 55% decreased risk of GBS-EOS for screening-based versus risk-based strategy (RR = 0.45; 95% CI: 0.34–0.59). For total early onset non-GBS sepsis (non-GBS-EOS), 7 studies with low heterogeneity (I ² = 18%) had a pooled RR of 0.91 (95% CI: 0.74–1.11), whereas for ampicillin resistant Escherichia coli-EOS, a subgroup of non-GBS-EOS, 3 studies with very low heterogeneity (I ² = 0%) had a pooled RR of 1.28 (95% CI: 0.74–2.21) for screening-based strategy compared with risk-based strategy. Conclusions: Compared with risk-based strategy, screening-based prophylaxis was associated with a reduced risk of GBS-EOS.

Background Primary neuroendocrine breast carcinomas (NEBCs) are an extremely rare and underrecognized subtype of mammalian carcinoma. The prognostic factors for NEBCs remain controversial. Methods In this multicenter retrospective study, the prognostic factors for patients with primary NEBCs who underwent surgery and had a pathologically confirmed diagnosis of neuroendocrine carcinoma in China and the United States were examined. The endpoints were disease-free survival (DFS) and overall survival (OS). Results A total of 51 Chinese patients and 98 US patients were included. In the Chinese cohort, tumor grade and Ki-67 levels were prognostic factors for DFS in univariate analysis (hazard ratio [HR] = 5.11 [1.67-15.60], p = 0.004; HR = 57.70 [6.36-523.40], p < 0.001, respectively) and multivariate analysis (HR = 100.52 [1.33-7570.21], p = 0.037; HR = 31.47 [1.05-945.82], p = 0.047, respectively). In the US cohort, age was an important prognostic factor for OS in univariate analysis (HR = 1.09 [1.04-1.15], p = 0.001). The random effects model for the combined cohorts revealed age and positive expression of estrogen receptor (ER) as potential prognostic factors for OS (HR = 1.08 [1.01-1.14], p = 0.015; HR = 0.10 [0.02-0.44], p = 0.003, respectively). Conclusions Tumor grade and Ki-67 levels are important prognostic factors for DFS of patients with primary NEBCs. Age and ER status are important prognostic factors for OS of patients with primary NEBCs.

Purpose: Numerous indicators can be used to predict tumor patients’ prognosis and tumor regression grade (TRG). The role of the neutrophil-lymphocyte ratio (NLR) among individuals with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) hasn't been studied, nevertheless. This study aims to explore the predictive value of the NLR before nCRT (pre-NLR) in TRG and prognosis of LARC patients undergoing nCRT.. Methods: In this retrospective investigation, 326 LARC patients receiving nCRT in total were included. The link between the pre-NLR and TRG was examined using a logistic regression analysis. A Cox-based nomogram was created in the meanwhile to forecast overall survival (OS). With the use of calibration plots and receiver operating characteristic (ROC) curves, we evaluated the nomogram's predictive capabilities. Results: The median pre-NLR across 326 patients was 2.2 (interquartile range, IQR: 1.7-2.7). In the logistic regression analysis, only the pre-NLR for TRG in LARC patients receiving nCRT was statistically significant (odds ratio, OR = 0.62, 95% CI: 0.47-0.80, P < 0.001). Pre-NLR, nCRT with surgery interval, ypTNM stage, TRG, vascular invasion, adjuvant chemotherapy, and carbohydrate antigen 19–9 before nCRT were revealed to be OS predictors in the Cox multivariate analysis. According to calibration plots and ROC curves, the predictive nomogram demonstrated high statistical performance on internal validation. Conclusion: This study demonstrated that a lower pre-NLR was probably associated with a greater rate of TRG in LARC patients undergoing nCRT. Furthermore, the pre-NLR was credibly correlated with OS in LARC patients undergoing nCRT. Meanwhile, we constructed a nomogram for predicting the prognosis in LARC patients undergoing nCRT.

The increasing burden of breast cancer has prompted a wide range of researchers to search for new prognostic markers. Considering that tumor mutation burden (TMB) is low and copy number alteration burden (CNAB) is high in breast cancer, we built a CNAB-based model using a public database and validated it with a Chinese population. We collected formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 breast cancer patients who were treated between 2010 and 2014 at the National Cancer Center (CICAMS). METABRIC and TCGA data were downloaded via cBioPortal. In total, 2295 patients with early-stage breast cancer were enrolled in the study, including 1427 in the METABRIC cohort, 837 in the TCGA cohort, and 31 in the CICAMS cohort. Based on the ROC curve, we consider 2.2 CNA/MBp as the threshold for the CNAB-high and CNAB-low groupings. In both the TCGA cohort and the CICAMS cohort, CNAB-high had a worse prognosis than CNAB-low. We further simplified this model by establishing a prognostic nomogram for early breast cancer patients by 11 core genes, and this nomogram was highly effective in both the TCGA cohort and the CICAMS cohort. We hope that this model will subsequently help clinicians with prognostic assessments.