The red blood cell distribution width-to-albumin ratio (RAR), an emerging biomarker integrating inflammation and nutritional status, has not been systematically evaluated for its prognostic value in breast cancer patients admitted to intensive care units (ICU). We conducted a retrospective cohort study using data from the MIMIC-IV 3.1 database, including 881 adult breast cancer patients admitted to the ICU. Patients were stratified into high- and low-RAR groups based on maximally selected rank statistics. Kaplan–Meier analysis, multivariable Cox proportional hazards models, restricted cubic splines, subgroup analysis, time-dependent concordance index (C-index) curves and Boruta feature selection (iterative random forest with shadow feature comparison) were applied to assess the association between RAR and 1-year all-cause mortality. Robustness was examined using E-value and propensity score weighting methods. Patients with high RAR (> 4.96) had significantly higher 1-year mortality compared to those with low RAR (log-rank P < 0.001). In adjusted models, high RAR independently predicted mortality (HR = 1.65, 95

Papillary thyroid carcinoma (PTC) poses a risk of recurrence, and the efficacy of existing treatments is limited. Consequently, there is an urgent need to identify new prognostic markers and potential therapeutic targets. N6-methyladenosine (m6A) mRNA methylation is involved in tumorigenesis and progression, yet the role of m6A RNA methylation regulators in PTC remains unclear. The Cancer Genome Atlas database was utilized to analyze 17 m6A regulators in PTC. Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was markedly down-regulated in PTC, yet higher IGF2BP1 expression predicts better 5-year survival, acting as an independent prognostic marker with high accuracy. Elevated IGF2BP1 also indicated greater sensitivity to doxorubicin and sunitinib. Clinically, low IGF2BP1 correlated with central lymph-node metastasis and BRAFV600E mutation. Additionally, IGF2BP1 overexpression suppresses thyroid carcinoma cell proliferation, invasion, and migration. In conclusion, High expression of IGF2BP1 was associated with a favorable prognosis in PTC, and it served as an independent prognostic factor and a potential therapeutic target for PTC.

Background:Estrogen receptor-positive (ER+) breast cancer, a prevalent subtype of breast malignancy, demonstrates complex etiological associations with multiple risk factors. Micronutrients, as essential nutritional components for human physiology, may potentially influence the pathogenesis and progression of breast carcinoma. This investigation employs Mendelian randomization (MR) methodology to assess causal relationships between 15 micronutrients and ER+ breast cancer. Methods:In this study, instrumental variables (IVs) for 15 micronutrients were extracted from the genome-wide association studies (GWAS) database, including copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, vitamin A, vitamin B12, vitamin B6, vitamin C, vitamin D, vitamin E, and zinc. Concurrently, summary data related to ER+ breast cancer were obtained from the FinnGen database. Following the selection of appropriate IVs, we conducted a two-sample MR analysis. This analytical framework incorporated comprehensive sensitivity analyses to evaluate potential heterogeneity and horizontal pleiotropy, with the inverse variance weighted (IVW) method established as the principal analytical approach. Results:The findings of our study revealed a significant causal relationship between vitamin B6 and ER+ breast cancer. Notably, genetically predicted elevated vitamin B6 levels were significantly associated with an increased risk of ER+ breast cancer [Odds Ratio (OR): 1.275; 95%Confidence Interval (CI): (1.017-1.600); P = 0.035]. In contrast, no statistically significant associations were observed between the other 14 micronutrients and ER+ breast cancer risk (P > 0.05 for all). Conclusion:Our results indicated that higher concentrations of vitamin B6 may be positively associated with ER+ breast cancer risk, and further research is needed to elucidate the underlying biological mechanisms of this association. This study provides new insights into understanding the role of micronutrients in breast cancer.

Identifying occult central lymph node metastasis (CLNM) is essential for guiding prophylactic lymph node dissection (PLND) in patients with cN0 stage papillary thyroid microcarcinoma (PTMC). This study aimed to identify molecular prognostic biomarkers associated with PTMC and develop a clinical-molecular prediction model for CLNM. Differentially expressed genes (DEGs) in PTMC were identified through bioinformatics analysis of the TCGA database. Prognostic DEGs were selected using Cox and LASSO regression analyses, and a risk-scoring model was constructed based on these genes. The prognostic value of the model was validated using Kaplan-Meier survival analysis and ROC curves. DEG expression levels were compared between patients with CLNM and those without (NCLNM). Clinical data and surgical specimens were collected from 404 patients with cN0 stage PTMC treated at the First Affiliated Hospital of Ningbo University in 2022. The cohort was randomly divided into a derivation cohort (n = 323) and a validation cohort (n = 81). DEG expression was quantified using RT-qPCR. Univariate and multivariate logistic regression analyses were conducted in the derivation cohort to identify predictors of CLNM and develop a predictive model. The model’s performance was evaluated using the Hosmer-Lemeshow test, ROC curves, calibration curves, and decision curve analysis (DCA). In the TCGA database, FN1, MT-1 F, and TFF3 were identified as prognostic biomarkers. Risk scores based on these genes achieved AUCs of 0.789 (5 years) and 0.674 (10 years) for predicting disease-free survival. Furthermore, FN1, MT-1 F, and TFF3 expression levels were significantly higher in the CLNM group compared to the NCLNM group. Among the 404 PTMC patients, the incidence of CLNM was 42.6

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

Background Gastric cancer is an aggressive disease with complex tumor heterogeneity, influenced by genomic, transcriptomic, and translational processes. N7-methylguanylate (m7G) modification is a potential factor in tumor heterogeneity, but its role in gastric cancer remains unclear. Methods We conducted cluster analysis on m7G-related genes in the TCGA-STAD dataset to identify gene patterns. Using LASSO regression, we identified genes that were differentially expressed in relation to m7G. A nomogram was developed to predict patient prognosis based on these genes. To further explore the molecular mechanisms, we performed survival analysis, functional enrichment analysis, and gene silencing experiments. Additionally, we examined tumor mutations, immune cell infiltration, and immune-related genes to evaluate immune responses and drug sensitivity. Results Clustering analysis identified two main gene groups linked to m7G modification. Survival analysis showed that high-risk patients, based on the selected genes, had poorer outcomes. Functional enrichment revealed that m7G modification influences tumor heterogeneity and the tumor microenvironment (TME), impacting prognosis. Silencing RASGRF2, the most significantly impacted gene, inhibited survival, proliferation, invasion, and migration of gastric cancer cells (AGS and HGC-27). Correlation analysis of immune responses and mutation patterns suggested that m7G modification affects the effectiveness of immunotherapy and drug sensitivity. Conclusions m7G modification plays a critical role in gastric cancer, influencing tumor heterogeneity and prognosis. The predictive nomogram provides a robust tool for forecasting patient survival, and targeting m7G modification could offer new therapeutic opportunities.

The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer. From the Surveillance, Epidemiology, and End Results (SEER) database, AYA women with primary breast cancer diagnosed from 2010 to 2018 were included in our study. A deep learning algorithm, referred to as DeepSurv, was used to construct a prognostic predictive model based on 19 variables, including demographic and clinical information. Harrell’s C-index, the receiver operating characteristic (ROC) curve, and calibration plots were adopted to comprehensively assess the predictive performance of the prognostic predictive model. Then, a novel clinical risk stratification was constructed based on the total risk score derived from the prognostic predictive model. The Kaplan–Meier method was used to plot survival curves for patients with different death risks, using the log-rank test to compared the survival disparities. Decision curve analyses (DCAs) were adopted to evaluate the clinical utility of the prognostic predictive model. Among 14,243 AYA women with breast cancer finally included in this study, 10,213 (71.7

Abstract Background The incidence of papillary thyroid carcinoma (PTC) has been increasing year by year, and its pathogenesis is not clear yet. The N6-methyladenosine (m6A) regulation has been proved to be related to the occurrence and development of the malignant tumors, but their expression patterns and prognostic effects in PTC remains unclear. Methods Data of 397 patients with PTC was downloaded from The Cancer Genome Atlas (TCGA) database. R language was used to analyze the relationship between the expression level of m6A RNA methylation regulators and clinicopathologic in PTC. LASSO Cox regression analysis was conducted to construct the risk prediction model and the area under ROC curve (AUC) was employed to evaluate the prediction accuracy of the model. Results Seventeen genes were screened out and identified as important regulators of m6A RNA methylation. It was found that m6A RNA methylation regulators were significantly correlated with T and N stage of PTC. The prediction model consisting of IGF2BP1, YTHDC2 and YTHDF3 genes was established by LASSO Cox regression analysis. Through univariate and multivariate analysis, IGF2BP1 was found to be an independent risk factor affecting the prognosis of PTC. Conclusions The m6A RNA methylation regulators are involved in the development and progression of PTC. Furthermore, the risk signature composed of three selected m6A RNA methylation regulators can be used as potential marker to predict prognosis in PTC.

Triple negative breast cancer (TNBC) is a subtype of breast cancer with strong aggressiveness and poor clinical treatment effect, accounting for about 10–20% of breast cancer cases. N(6)-methyldeoxyadenosine (6mA) is the most conservative DNA modification in prokaryotes and eukaryotes. It is widely found in bacteria and has such functions as DNA mismatch repair, chromosome separation and virulence regulation. We determined that 6mA was modified in TNBC cell line MDA-MB-231 and the TNBC tissue. Meanwhile, compared with normal tissues, the expression level of 6mA and its methylase N6AMT1 was significantly decreased in TNBC tissue. MDA-MB-231cells were cultured with 8μM Olaparib for 2 months to construct drug-resistant cell line 231-RO. It was found that the level of 6mA also increased significantly, and the expression of N6AMT1 or ALKBH1 could effectively influence the drug resistance. Subsequently, we found that LINP1 was highly expressed in 231-RO, which was involved in DNA repair, and the expression of LINP1 could be positively regulated by 6mA modification. LINP1 expression level is directly related to TNBC drug resistance. The above results indicate that 6mA may be a new biological marker of TNBC. Meanwhile, 6mA modification may be involved in the regulation of Olaparib resistance.