BMC Cancer最新文献

Purpose: To evaluate the efficacy and safety of systemic treatment combined with radiotherapy (RT) as the first-line treatment for de novo advanced esophageal cancer (EC).

Methods: A meta-analysis was conducted, and it followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. A literature search was performed systematically in PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science on February 1, 2025. The protocol of this meta-analysis was published in PROSPERO with the registration number CRD42025650118.

Results: Eight studies involving a total of 11,356 patients were finally included. Systemic treatment combined with RT improved OS (HR = 0.72, 95% CI: 0.70-0.74, P < 0.001) and PFS (HR = 0.70, 95% CI: 0.62-0.78, P < 0.001) compared with systemic treatment alone. The grade ≥ 3 treatment-related lymphopenia (OR = 5.52, P < 0.001), leukopenia (OR = 1.56, P < 0.001), and esophagitis (OR = 13.11, P < 0.001) were more frequent in the RT-combined group; no significant differences were observed in other severe toxicities. Subgroup analysis on systemic treatment type, ESCC, and TNM stage edition also demonstrated that this RT-combined treatment could provide significant survival benefits. Exploratory analysis showed that maximal survival benefit emerged in patients who received systemic therapy, especially immunochemotherapy, combined with radical (≥ 50 Gy) primary tumor RT.

Conclusions: By synthesizing data from both the pre-immunotherapy and immunotherapy eras involving 11,356 patients, we found that the incorporation of radical radiotherapy into first-line systemic treatment regimens improves survival outcomes while maintaining acceptable toxicity profiles in selected patients with advanced EC.Further randomized clinical trials are needed to verify our conclusions.

Background: Oral cancer remains a major global health issue, with timely diagnosis being essential due to its varied clinical presentation. This study explores how artificial intelligence (AI) can support early detection by analyzing intraoral photographs.

Methods: A cross-sectional analysis was performed using 518 intraoral clinical images collected from the Department of Oral Medicine, Kerman Faculty of Dentistry, between 2009 and 2023. The dataset comprised 104 images of malignant lesions and 414 of benign or normal tissue, all confirmed by a specialist in oral pathology. Three pretrained deep learning models, DenseNet-121, EfficientNet-B0, and ResNet-50, were evaluated for their ability to classify lesions as malignant or benign. The data were split into training (80%) and testing (20%) sets, with preprocessing completed before analysis.

Results: Among the models, DenseNet-121 demonstrated superior performance, achieving 91% accuracy, 75% sensitivity, 98% specificity, 75% positive predictive value, 96% negative predictive value, an F1 score of 84%, and an area under the curve of 90%. These results exceeded the diagnostic accuracy of an experienced oral specialist.

Conclusion: AI-based analysis of clinical images can significantly improve early oral cancer detection and should be integrated into clinical workflows to enhance diagnostic precision.

Background: Inflammatory blood markers and tumor-infiltrating lymphocytes (TILs) are associated with the prognosis of various cancers. However, reports on their value as prognostic markers in soft tissue undifferentiated pleomorphic sarcoma (UPS) are limited. We aimed to clarify the predictive value of these markers for the prognosis of patients with UPS, focusing on resectable tumors of the extremities and trunk.

Methods: This retrospective analysis included data from 103 patients with localized UPS in the extremities and trunk. The neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and systemic inflammatory response index (SIRI) were calculated, and the median values were determined as cut-off values. Immunohistochemical staining of CD8 + TILs was also performed. Disease-specific overall survival (DOS) and distant metastasis-free survival (DMFS) rates were analyzed using the Kaplan-Meier method, and prognostic factors were identified using the Cox proportional hazards model.

Results: Among the inflammatory blood markers, SIRI was found to be a sensitive prognostic factor, and a high SIRI was associated with worse DOS (P = 0.16) and DMFS (P = 0.09). Although CD8 + TILs were not associated with DOS (P = 0.67), high CD8 + TILs correlated with improved DMFS (P = 0.20). Only tumor size > 10 cm was significantly associated with worse DMFS (P = 0.02) in the univariate analysis, while inflammatory blood markers and CD8 + TIL showed no correlation. The combination of SIRI and CD8 + TILs revealed that high CD8 + TILs in tumor tissue could improve DMFS (P = 0.04) and DOS (P = 0.15) in patients with high SIRI compared to those with low CD8 + TILs and high SIRI.

Conclusions: In patients with localized UPS, CD8 + TILs infiltration into the tumor tissue could improve the prognosis of patients with high SIRI.

Objective: This study aimed to evaluate the clinical and laboratory factors affecting treatment response and treatment tolerance in patients diagnosed with head and neck squamous cell carcinoma (HNSCC) and treated with chemoradiotherapy (CRT) after three cycles of induction chemotherapy.

Method: Patients who were followed up in the oncology clinic between January 2014 and December 2024 and who could not undergo an organ-sparing approach or surgery were analyzed retrospectively. Demographic, clinical, biochemical and inflammatory parameters of the patients were examined. Binary-logistic regression analysis was used for patients who could not complete the treatment due to toxicity, Cox-regression analysis was used to investigate the factors affecting overall survival (OS), and Roc-curve analysis was used to determine the ideal-cut-off values ​​for blood markers.

Results: A total of 92 patients with HNSCC were included in the study. In univariate logistic regression analysis, age ≥ 60 (p = 0.006), presence of comorbidity (p = 0.029), body mass index (BMI) < 23.25 (p = 0.005), poor ECOG performance score (2-3) (p < 0.001) and low prognostic nutritional index (PNI) (p = 0.011) were found to be significant risk factors for not completing treatment. Multivariate logistic regression of age, BMI, ECOG, and PNI together formed a predictive model for treatment incompletion. In Cox regression analysis, BMI <23.25 (p=0.016), poor ECOG performance score (2-3) (p=0.002), advanced disease stage (p=0.002), and low PNI (<51.1) (p=0.006) were the main risk factors for unfavorable overall survival. Gender, smoking, tumor location, treatment regimen, and hematological parameters had no significant effect on survival and treatment completion.

Conclusion: In HNSCC patients who underwent post-induction CRT, nutritional parameters such as BMI and PNI and performance status play a determining role on treatment tolerance and survival. Detailed assessment of nutritional status before treatment may have an impact on treatment success and survival.

This study explores the non-invasive prediction of MKI-67 (Ki67) expression status in breast cancer using preoperative ultrasound image heterogeneity. Data from 432 patients (training set) and 109 (test set) across two medical institutions were analyzed. Tumor regions were automatically outlined using the Swin-unet network, and habitat clustering within these regions was performed using the k-means method. Radiomics and deep learning features (ResNet-101) were extracted from both global tumor regions and habitat subregions. Laboratory data were integrated, followed by the Least Absolute Shrinkage and Selection Operator (LASSO) feature reduction and machine learning modeling to predict Ki67 expression status. Model performance was evaluated using accuracy (Acc), area under the curve (AUC) with 95% confidence intervals (CI), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), calibration curves, confusion matrices, and decision curves. The DeLong test was used to compare the diagnostic performance of the composite model with individual models. The results showed that the combined model (Habitat + Global + Laboratory + Deep Learning) achieved the best predictive performance, with Acc, AUC, Sen, Spe, PPV, and NPV of 0.798, 0.838, 0.780, 0.809, 0.711, and 0.859, respectively, in the test set. Calibration curves and confusion matrices confirmed the model's robustness, while decision curves demonstrated its clinical utility. The DeLong test confirmed the composite model's significantly superior AUC compared to several individual models, though not all combined models showed significant differences. However, despite not showing significant advantages in comparisons with some combined models, the composite model, leveraging its unique strength of comprehensively integrating multi-dimensional features, has demonstrated stronger adaptability and stability in real-world clinical application scenarios, providing more reliable support for accurate prediction. In conclusion, preoperative ultrasound image heterogeneity, through the integration of habitat subregion, global tumor, laboratory, and deep learning features, provides valuable insights for predicting Ki67 expression status in breast cancer, enhancing routine preoperative ultrasonography and offering a potential non-invasive method for preoperative Ki67 prediction.

Background: Immune checkpoint inhibitors (ICIs) improve the outcomes across solid tumours, although they can cause severe immune-related adverse events (irAEs). Due to this possibility of side effects, practical and low-cost predictors of severe irAEs are needed to guide patient monitoring and care.

Methods: We conducted a single-centre retrospective cohort study involving 593 patients who were treated with anti-PD-1/PD-L1 monotherapy or anti-PD-1/PD-L1 plus anti-CTLA-4 combination therapy from June 2016 to November 2024. The primary endpoint was the time to the first severe irAE (grade ≥ 3). Peripheral blood biomarkers were evaluated at baseline and immediately before the cycle 3. The cumulative incidence was estimated, and the associations were quantified using the Fine-Gray subdistribution hazards ratio (sHR) model in a competing risks framework.

Results: Overall, 11.6% of patients experienced a severe irAE, with the median time to the first event being 12 weeks and the most frequent severe irAE being colitis (n = 21; 3.5%). Combination therapy was associated with a higher risk when compared with monotherapy (sHR 3.71, 95% confidence interval [CI] 2.25-6.13). Baseline eosinophils > 250/µL were associated with an increased risk (sHR 2.22, 95% CI 1.35-3.65). A lower red cell distribution width (RDW) was likewise associated with the risk at two timepoints: baseline RDW ≤ 15.8% (sHR 2.60, 95% CI 1.35-5.01) and pre-cycle 3 RDW ≤ 14.3% (sHR 2.71, 95% CI 1.44-5.09). The effects were directionally consistent across subgroups, and no interactions were detected. The other blood biomarkers tested were not significant (all p > 0.05).

Conclusions: A high baseline eosinophil count and a lower RDW early on during therapy identify patients at increased risk of severe irAEs. These accessible measures could support personalised monitoring and biomarker-guided patient selection. However, external validation is needed to confirm the robustness and validate the thresholds identified in this study prior to clinical use.

Background: Glioma is the most common primary malignant tumor of the central nervous system and is associated with an extremely poor prognosis. Given its highly complex molecular landscape, there is an urgent need to identify novel diagnostic biomarkers. This study systematically identified core glioma genes through multi-cohort integration and single-cell analysis.

Methods: Five Gene Expression Omnibus (GEO) transcriptomic datasets (GSE109857, GSE15824, GSE35158, GSE4290, and GSE90886; 504 samples) were integrated, and batch effects were corrected using ComBat. Differentially expressed genes (DEGs) were identified and combined with weighted gene co-expression network analysis (WGCNA) to obtain candidate modules. Feature genes were further screened using multiple approaches, including CytoHubba, least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF) analysis. Validation was performed using external GEO datasets, receiver operating characteristic (ROC) analysis, and quantitative real-time polymerase chain reaction (qRT-PCR) based on 69 clinical samples. Additional analyses included gene set enrichment analysis (GSEA), CIBERSORT-based immune infiltration profiling, expression mapping across three single-cell RNA sequencing (scRNA-seq) datasets (GSE273274, GSE162631, and GSE182109), and construction of competing endogenous RNA (ceRNA) and transcription factor (TF) networks, as well as drug enrichment analysis.

Results: A total of 409 glioma-related candidate genes were identified. Six hub genes, SYP, SYN1, RAB3A, SLC17A7, SYN2, and STXBP1, were consistently identified across all algorithms. All six genes were significantly downregulated in glioma, as confirmed by GEO datasets (area under the curve [AUC]: 0.787-0.802) and clinical qRT-PCR validation. GSEA revealed that low expression of these genes was associated with activation of the cell cycle, complement and coagulation cascades, and immune dysregulation. Immune infiltration analysis showed negative correlations with M1/M2 macrophages and activated natural killer (NK) cells. Single-cell analyses indicated that these hub genes were primarily enriched in tumor-propagating cell (TPC)-like tumor cells but were markedly reduced in glioma core regions. Competing endogenous RNA (ceRNA)/TF networks and drug enrichment analyses suggested multilayered regulatory mechanisms and associations with neurotransmitter-related compounds.

Conclusion: The six identified hub genes are consistently downregulated in glioma and exhibit strong diagnostic potential. Their close association with the immune microenvironment and tumor-cell lineage highlights their value as biomarkers and potential therapeutic targets.