Background Endometrial carcinoma (EC) is one of the most common gynecological malignancies. Aberrant DNA methylation (DNAm) is an important epigenetic alteration involved in cancer progression. Long non-coding RNAs (LncRNAs) play critical roles in gene regulation and pathological processes. However, the biological and prognostic significance of lncRNA-mRNA pairs associated with methylation remains unclear.Methods Transcriptomic, DNA methylation, and clinical data from The Cancer Genome Atlas were integrated to construct a differentially methylated sites (DMSs)-lncRNA-mRNA network. After mapping DMSs to their corresponding differentially methylated genes (DMGs), the DMS-mediated network was further represented as a DMG-lncRNA-mRNA tripartite network. After removal of DMG nodes, a DMG-associated lncRNA-mRNA background network was constructed, and key lncRNA-mRNA pairs were prioritized according to their topological importance using weighted shortest-path calculation and PageRank-based random walk algorithms. The methylation-associated lncRNA- mRNA pair (MLMP) model was then established based on univariate Cox, LASSO Cox, and multivariate Cox regression analyses.Results Eight DMG-associated lncRNA-mRNA pairs were selected to construct the MLMP model. Under a common analysis framework, the MLMP model achieved higher area under the curve (AUC) values than 17 previously published prognostic signatures for overall survival prediction. Patients in the low-MLMP group had significantly better overall survival than those in the high-MLMP group. Functional enrichment and immune microenvironment analyses indicated that the MLMP model was associated with immune-related pathways and distinct tumor immune microenvironment characteristics. Predictive analyses suggested that patients in the low-MLMP group exhibited higher predicted responsiveness to CTLA-4/PD-1 blockade and differential predicted sensitivity to several therapeutic agents.Conclusions The MLMP model may serve as a potential tool for prognostic stratification in EC and provide insights into methylation-associated lncRNA-mRNA interactions. The predicted associations with immunotherapy response and therapeutic sensitivity may generate hypotheses for future treatment-related studies. These findings require further validation in independent clinical cohorts and experimental studies before clinical application.
Objective The use of evidence in clinical practice is essential for the provision of best practice care in psychosocial oncology. However, research demonstrates gaps in the implementation of evidence-based interventions and important resource barriers for clinicians. Clinical practice guidelines (CPGs) are designed to translate the latest evidence into recommendations applicable within various clinical domains. A recent study identified 23 English-language, up-to-date psychosocial oncology CPGs. The present study sought to systematically assess the quality of these tools.Methods CPGs were independently evaluated by four trained appraisers using the AGREE II tool, consisting of 23 items across six domains. Quality was determined using aggregate and consensus domain scores (0%-100%), with an acceptability cut-off score of 60%.Results Findings revealed that 20 of the 23 CPGs (87%) met the acceptability threshold (M = 76.45, SD = 12.35). The highest average domain scores were observed for Clarity of Presentation (M = 96.26, SD = 6.25) and Scope and Purpose (M = 87.38, SD = 19.55) while Applicability received the lowest score (M = 33.83, SD = 10.72). Rigor of Development scores (M = 71.17, SD = 17.46) demonstrated substantial variations across CPGs.Conclusions Findings indicate that psychosocial oncology CPGs are specific and clear but may benefit from more transparency in reporting procedures and greater consideration of applicability and implementation strategies. These findings may help illuminate guideline developers on gaps in current tools and serve to inform clinicians on the strengths and shortcomings of these tools prior to selecting which ones to integrate within their clinical practice.
Background Relapsed or refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) patients exhibit heterogeneous responses to CD19 chimeric antigen receptor T-cell (CAR-T) therapy. However, current studies primarily focus on the CAR-T cells or the leukemia blasts, and the patient's intrinsic molecular characteristics before treatment remain poorly understood.Methods Fifteen R/R B-ALL patients enrolled in a phase II, single-arm clinical trial (NCT04684147) were stratified at a three-month landmark into 8 good responders and 7 poor responders. Clinical characteristics, genetic alterations and pre-treatment bone marrow transcriptomics were compared between groups, and an exploratory gene-expression score was derived.Results Differential expression and enrichment analyses identified a myeloid-like signature and upregulation of myeloid-related genes in poor responders. Seven myeloid-related genes were examined in independent patient cohorts, in B-ALL cell lines and at single-cell resolution in one patient; three were expressed by the leukemic cells themselves and four predominantly by monocytes and macrophages. A separate, variance-derived seven-gene least absolute shrinkage and selection operator (LASSO) score separated response groups in the discovery cohort (apparent area under the curve, AUC 1.00), with bias-corrected and external estimates of approximately 0.57-0.59 (external cohort AUC 0.592, 95% confidence interval 0.356-0.828).Conclusions Pre-treatment transcriptomic profiling revealed a myeloid-like signature in R/R B-ALL patients with poor CD19 CAR-T response, warranting further investigation as a candidate correlate of treatment outcome.
Background Combination therapy with immune checkpoint inhibitors and tyrosine kinase inhibitors (IO-TKI) is a key treatment option for metastatic renal cell carcinoma (mRCC), but distinguishing immune-related adverse events (irAEs) from TKI-related adverse events (TKI-AEs) is challenging because of overlapping clinical features. We aimed to evaluate whether eosinophil counts can help differentiate between them. Methods In this retrospective multicenter study, 123 patients with mRCC treated with IO-TKI therapy were analyzed. Patients were classified into an irAE group (n = 64) and a TKI-AE group (n = 59). Changes in eosinophil counts from baseline to the start of the second treatment cycle were compared. Receiver operating characteristic (ROC) and logistic regression analyses were performed to assess associations with irAE occurrence. Results Baseline eosinophil counts did not differ between groups. Eosinophil counts at the start of the second treatment cycle were higher in the irAE group than those in the TKI-AE group (269.6 vs. 189.6 eosinophils/mu L; p < 0.05). No significant differences were observed in other blood parameters, including neutrophil, lymphocyte, monocyte, and platelet counts, the neutrophil-to-lymphocyte ratio, or the platelet-to-lymphocyte ratio. Although the overall discriminatory performance was modest (area under the ROC curve = 0.57, sensitivity = 0.30, specificity = 0.94), eosinophil counts >= 380/mu L were associated with irAEs in both univariate (odds ratio = 7.21) and multivariable (odds ratio = 8.39; p < 0.05) analyses. Conclusions Early increases in eosinophil counts were associated with irAEs and may help distinguish irAEs from TKI-AEs during IO-TKI therapy. Given their high specificity but limited sensitivity, eosinophil counts may have utility as an adjunctive rather than stand-alone marker, and prospective validation is warranted.
ABSTRACT Background Multiple myeloma (MM) exhibits significant heterogeneity. Traditional risk stratification often relies on static baseline data, failing to capture the dynamic evolution of biomarkers. This study aimed to develop a prognostic model incorporating longitudinal biomarker trajectories to improve risk stratification for newly diagnosed MM patients. Methods We retrospectively analyzed 635 patients from West China Hospital between February 1, 2017 and December 30, 2022. Biomarkers including M protein, free light chains (FLC), creatinine (Cr), hemoglobin (Hb), immunoglobulin levels, and others were collected monthly within the first‐year post‐treatment. Patients were randomly divided into an internal training (70%) and validation (30%) cohort. Group‐based trajectory modeling (GBTM) and Kaplan–Meier analysis were used to identify distinct biomarker evolution patterns, and the GBTM risk model was validated by the lowest mean squared error (LMSE) method. Cox regression analysis and a nomogram were employed to develop a prognostic model to provide individualized survival probability estimates and were validated in the internal validation cohort using time‐dependent ROC analysis. Results GBTM identified dFLC, IgM, Cr, and Hb trajectories as significant prognostic factors. Patients scoring ≥ 3 (high‐risk) showed significantly poorer overall survival than those scoring < 3 (low‐risk) in both the training (median OS 32.2 months vs. not reached, p < 0.0001) and validation cohorts (36.8 months vs. not reached, p < 0.005). Then we established a nomogram that included the GBTM risk group, ISS stage, age, frontline treatment, pulmonary disease, cardiovascular disease, and thrombotic events. The nomogram demonstrated good predictive performance with AUC values consistently above 0.7 for 2‐ and 3‐year survival in both cohorts (Training: 0.71, 0.70; Validation: 0.70, 0.70). Conclusion Our study successfully developed a prognostic risk stratification model for MM patients using biomarker trajectories. This model may help identify high‐risk patients and predict long‐term survival, offering a valuable tool for personalized treatment planning.
