Cancer Medicine最新文献

Background: Anti-programmed death 1 (PD-1) and anti-programmed death ligand 1 (PD-L1) immune checkpoint inhibitors (ICIs) have changed the treatment landscape of many advanced malignancies. However, immune-related adverse events (irAEs) bring great challenges to clinical benefits. The prediction of irAEs is urgently demanded for early detection and intervention.

Methods: Patients in our center who received anti-PD-(L)1 immunotherapy between January 2019 and May 2023 were collected. Logistic least absolute shrinkage and selection operator (LASSO) regression analysis with 10-fold cross-validation was performed to identify the most relevant variables associated with irAEs. Multivariate logistic regression analysis was used to build a prediction model by introducing features selected in LASSO regression analysis.

Results: Overall, 680 eligible patients were included, of whom 330 patients were included in the irAEs group. In the irAEs group, 455 different irAEs were reported, of which 52 events were grade 3 or higher in severity. Endocrinal toxicities (174/680, 25.59%) were the most commonly reported irAEs. Through LASSO and logistic regression analysis, we developed a risk assessment model to predict the risk of irAEs based on basophil percentage (BASO%), hemoglobin (Hb), absolute lymphocyte count (ALC), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), blood urea nitrogen level (BUN), the Charlson comorbidity index (CCI) score, Eastern Cooperative Oncology Group Performance Status (ECOG PS), and hepatitis B/hepatitis B surface antigen carriers. The model had a C-index of 0.727, with good discrimination and calibration capabilities.

Conclusion: The prediction model developed in our study can screen and monitor patients with high risk of developing irAEs. It may improve prognosis for pan-cancer patients receiving anti-PD-(L)1 immunotherapy.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality with limited therapies. Reprogrammed lipid metabolism, driven by upregulated de novo lipogenesis, is a key tumorigenic mechanism. The enzyme ACSL3 is strongly correlated with poor HCC prognosis, positioning it as a potential therapeutic target.

Material and methods: ACSL3 expression was assessed in vivo and ex vivo using comparative analysis. Bioinformatic investigations, including gene set enrichment analysis (GSEA) and KEGG pathway analysis, were employed to identify signaling pathways and biological processes associated with ACSL3 overexpression.

Results: ACSL3 expression was consistently elevated in HCC models. Enrichment analyses revealed that high ACSL3 levels are associated with activation of the STAT3 signaling pathway and upregulation of key lipogenic enzymes, suggesting a feedforward oncogenic loop. Predictive data also indicate a correlation between ACSL3 expression and the immune checkpoint regulator PD-L1.

Conclusion: These findings underscore ACSL3 as a significant biomarker and candidate therapeutic target in HCC. Its role bridges dysregulated lipid metabolism with oncogenic signaling and immune evasion, warranting further investigation into ACSL3-targeted strategies.

Background: Systemic inflammatory biomarkers such as C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are increasingly studied in breast cancer, but their within-treatment dynamics and relationship with anthropometric context during chemotherapy remain underexplored. This study aimed to evaluate early-to-intermediate, within-treatment changes in inflammatory biomarkers (CRP, NLR, PLR) and examine their associations with sociodemographic, clinical, and anthropometric variables among women with stage I-III non-metastatic breast cancer receiving outpatient chemotherapy, measured immediately before the first (C1) and third (C3) cycles.

Material and methods: Prospective single-arm cohort of women with stage I-III breast cancer receiving outpatient chemotherapy at a single center. Biomarkers were measured immediately before the first infusion (C1) and before the third cycle (C3). Nutritional status was assessed anthropometrically (BMI, waist circumference, triceps skinfold thickness, arm circumference, corrected arm muscle area). Primary analyses modeled biomarkers as continuous outcomes in linear mixed models (LMMs) including time (C1 vs. C3) and covariates significant in bivariate tests; effect sizes were estimated using Glass's Delta. We also assessed pairwise correlations among biomarkers within C1 and C3 and temporal stability (C1 ↔ C3) using Spearman's rho with FDR control.

Results: CRP was elevated in 26/30 (86.7%) at C1 and remained high at C3; the time effect was not significant in adjusted models (ANOVA p = 0.951). Mean NLR and PLR were below common clinical thresholds at both time points but trended upward; PLR increased in crude paired testing (p = 0.049), yet the adjusted time effect was not significant (p = 0.468). Effect sizes were tiny for NLR (Δ = 0.08) and CRP (Δ = 0.007) and small for PLR (Δ = 0.20). In multivariable analyses, BMI remained associated with higher CRP (ANOVA p = 0.012). Inter-marker correlations within C1 and C3 were small and not significant after FDR adjustment. CRP showed moderate temporal stability between C1 and C3 (ρ = 0.628; q = 0.003), whereas NLR (ρ = 0.327; q = 0.300) and PLR (ρ = 0.325; q = 0.300) were positive but not statistically significant.

Conclusion: Early within-treatment monitoring revealed a stable elevation of CRP and modest upward trends in NLR/PLR from C1 to C3, with BMI associated with CRP after adjustment. CRP also exhibited greater short-term stability than NLR/PLR. Although limited by small sample size and two time points, these findings are hypothesis-generating and support larger, multi-center studies with denser sampling and longer follow-up to clarify prognostic value and inform personalized supportive care.