Technology in Cancer Research & Treatment最新文献

BackgroundTumor necrosis factor receptor superfamily member (10B TNFRSF10B), as a key apoptosis regulator of Oral Squamous Cell Carcinoma (OSCC), exerts a critical effect on its development.MethodsDifferentially expressed genes in OSCC (GSE25099) were screened first. Weighted gene co-expression network analysis identified gene modules, followed by Lasso regression and Cox modeling to pinpoint pivotal genes. Expression was validated in the Cancer Genome Atlas databases and in clinical samples. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used to generate a protein-protein interaction (PPI) network, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses explored biological functions. Then, for in vitro assays, core gene-targeted siRNAs were introduced into SCC-4 and SCC-9 cell lines to mediate gene knockdown. Cell proliferation was quantified by the CCK-8 method, and apoptotic activity was assessed via flow cytometry, TUNEL staining, and Western blotting for apoptosis-associated proteins.ResultsAmong the 10 core genes that were further screened, TNFRSF10B was most notably linked to unfavorable OSCC prognosis and showed strong diagnostic power. Additionally, its overexpression was associated with clinical stage, nodal metastasis, and chemoresistance. PPI and enrichment analyses revealed its role in extrinsic and necroptotic apoptosis. Moreover, the knockdown of TNFRSF10B suppressed viability and induced apoptosis by upregulating Bax, downregulating Bcl-2, and activating Caspase-3/PARP.ConclusionsTNFRSF10B drives OSCC progression by impairing apoptosis. Its overexpression correlates with poor prognosis and represents a potential diagnostic and therapeutic target. Furthermore, targeting TNFRSF10B may restore apoptosis, thus making precision therapy achievable.

IntroductionNon-small cell lung cancer (NSCLC) is the most prevalent and lethal subtype of lung cancer. Most patients are diagnosed at an advanced stage of the disease, resulting in a poor prognosis. Early treatment and clinical intervention for NSCLC following early diagnosis can improve patients' survival rate. It is of considerable significance to develop a more efficient and precise approach for identifying key genes and clinically pertinent biomarkers in NSCLC to enable its early diagnosis.MethodsAn interpretable two-stage analytical framework integrated with advanced artificial intelligence (AI) technology is proposed to enhance the accuracy of biological gene screening for NSCLC. Firstly, gene-level statistical features derived from the GSE19804,GSE30219 and GSE33532 datasets are standardized and dimensionally reduced via principal component analysis (PCA), which reveals two distinct linear distribution patterns of candidate genes in the PCA projection space. Subsequently, these candidate genes are validated using the TCGA and GEPIA platform by evaluating their differential expression profiles and associations with patient survival outcomes, with the goal of identifying robust predictive biomarkers.ResultsThrough AI-driven analytical pipelines, multiple tumor-associated genes are screened and confirmed to be correlated with NSCLC progression. Notably, ADGRD1 (Adhesion G Protein-Coupled Receptor D1) exhibits a close association with pulmonary physiological functions and may serve as a potential biomarker in the initiation and progression of NSCLC.ConclusionThe proposed method combines unsupervised structural discovery with cross-cohort clinical evidence to prioritize NSCLC biomarkers, providing critical support for early diagnosis, prognostic stratification, and biomarker-guided therapeutic strategies. Furthermore, the study provides technical support for biomarker discovery in other cancer types, and highlights the application value of integrating computational intelligence with oncology research.

ObjectiveTo validate the diagnostic performance of anti-BNLF2b antibody for detecting nasopharyngeal carcinoma (NPC) compared with healthy controls (HC).MethodsWe conducted a retrospective study including 220 patients with NPC, 61 with tongue cancer (TC), and 88 HC patients. We collected demographic and clinical data, including anti-BNLF2b antibody, EBV DNA, VCA-IgA, EBNA1-IgA, and Rta-IgG. Propensity score matching (PSM) was used to balance baseline characteristics between NPC and comparison groups. Associations between biomarkers and NPC diagnosis were examined using logistic regression. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis.ResultsAfter PSM, 88 patients with NPC were matched to 88 HC with balanced baseline characteristics. Anti-BNLF2b antibody levels were significantly higher in NPC and remained independently associated with NPC diagnosis. For NPC versus HC, anti-BNLF2b antibody showed excellent discrimination (AUC = 0.990; sensitivity 98.9%; specificity 92.0% at a cut-off of 0.210), exceeding the performance of the EBV dual antibody risk probability (AUC: 0.872; P < 0.001). In addition, patients with NPC had higher anti-BNLF2b antibody levels and other EBV-related markers than those with TC.ConclusionIn this retrospective study, anti-BNLF2b antibody demonstrated excellent discrimination for NPC. It may serve as a complementary serologic marker, pending external validation and prospective assessment of clinically optimized cutoffs.

IntroductionTo develop and evaluate a multi-omics machine-learning model that integrates clinical variables, dose-volume histogram (DVH) metrics, radiomics, and dosiomics from both the rectum and rectal wall regions of interest (ROIs) to improve prediction of acute radiation proctitis (ARP) in cervical cancer patients receiving radiotherapy.MethodsIn this single-center retrospective cohort, 107 cervical cancer patients were randomly split into a training set (n = 85) and a testing set (n = 22) in an 8:2 ratio. Radiomic were extracted from planning CT, and dosiomic features from 3-D RT-dose distributions, for both rectum and rectal wall ROIs. Features were z-score standardized; redundant features were filtered by Pearson correlation, followed by least absolute shrinkage and selection operator (LASSO) for selection. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers were trained using stratified five-fold cross-validation within the training set. Model performance was assessed on the held-out test set using receiver operating characteristic (ROC) analysis; clinical utility was evaluated with decision-curve analysis (DCA). The primary endpoint was Common Terminology Criteria for Adverse Events (CTCAE,version 5.0) grade ≥2 ARP.ResultsMulti-omics fusion outperformed single-modality models across ROIs and classifiers. The rectal-wall multi-omics SVM achieved the best discrimination with AUC 0.867 (95% Confidence Interval [CI]:0.709-1.000) in the test set; performance for the whole-rectum region of interest (ROI) was lower (AUC 0.714). DVH-only models showed limited discrimination, and no DVH feature was retained after penalized selection in the multi-omics pipeline. DCA demonstrated the greatest net clinical benefit for the rectal-wall multi-omics model across threshold probabilities 0.20-0.50.ConclusionA rectal-wall, region-specific multi-omics approach integrating clinical, radiomic, and dose-based descriptors improves prediction of radiotherapy-induced ARP compared with single-modality and whole-rectum analyses. These findings highlight the importance of ROI selection and multi-omics integration for precision toxicity assessment and support future external validation and prospective evaluation.

Small cell lung cancer (SCLC) is an aggressive malignancy with poor prognosis. No validated prognostic score has been established to guide clinical decisions in the extensive stage (ES). This narrative review critically examines the evolution of prognostic models in SCLC. We aim to highlight current gaps and propose directions for the development of clinically actionable tools. We conducted a comprehensive review of the literature on SCLC prognostic models, focusing on historical context, model design, variables used, validation methods, and real-world applicability. Comparative strengths and limitations were analysed across different model types. We analysed early scoring systems, modern nomograms, inflammation-based and nutritional scores, as well as integrative models. Historical tools are often limited to disease stage, performance status, basic laboratory values, most lack external validation, are retrospective, or were developed on chemotherapy-only cohorts. Recent models incorporate broader clinical data and, in some cases, nomograms or web-based calculators. Yet, few have undergone external validation or demonstrated utility in diverse clinical settings. The absence of dynamic, personalized models prevents integration into contemporary practice. Although numerous prognostic tools have been proposed, a reliable, validated tool is still lacking. Future prognostic models must move beyond static clinical parameters. Incorporating molecular biomarkers, real-world data, and machine learning could enable the development of validated, adaptive tools with true clinical relevance. Collaborative, prospective efforts will be critical to achieve this goal.

IntroductionBlood perfusion insufficiency and hypoxia are the main causes of drug resistance to chemotherapy in breast cancer. Increasing blood perfusion can improve drug delivery. This study aimed to investigate the effects of ultrasound-stimulated microbubbles (USMBs) on hemoperfusion in invasive breast cancer (IBC).MethodsIn this prospective clinical trial, 36 patients diagnosed with IBC were enrolled sequentially. The treatment group (n = 18, enrolled from June 2022 to April 2025) were treated with SonoVue^® microbubbles (MBs) stimulated by ultrasound, with a mechanical index (MI) of 0.2-0.3: 1 mL of SonoVue^® MBs was injected at 3.5-min intervals three times for a USMB treatment lasting 10 min. The control group (n = 18, enrolled from May to November 2025) received identical MB injections without ultrasound stimulation. Contrast-enhanced ultrasound (CEUS) was used to evaluate the changes in blood perfusion.ResultsIn the treatment group, in comparison with the pre-treatment findings, the tumor perfusion area expanded (P < .001) and the time to peak (TTP) increased (P < .05) after USMB treatment. For regions exhibiting low enhancement inside the lesion on CEUS before USMB treatment, the area under the curve (AUC) (P < .001) and mean transit time (MTT) (P < .05) both increased following therapy. In the control group, none of the parameters showed statistically significant differences after the MB injections.ConclusionUSMB treatment can improve blood perfusion in IBC, especially by enhancing the AUC and MTT in hypoperfused regions. These findings highlight the potential of USMB treatment as a noninvasive technique to enhance intratumoral drug delivery, although further validation of this approach is required.Clinical trial registration number: NCT06158217.

IntroductionCommercial automatic planning modules are currently limited to single, conventional disease types, which severely restricts their utility when dealing with unconventional plans. Given that such unconventional plans are actually the norm in most hospitals, there is an urgent need for an automatic planning algorithm that can be applied to a wide range of clinical situations. To address this issue, we developed an algorithm capable of automatically cropping target areas and setting optimization conditions for multiple diseases, known as the Auto Crop and Optimization Setup Algorithm (ACOSA). This paper presents the principles of ACOSA and conducts a preliminary comparative evaluation of its performance against existing solutions.MethodsThe development of ACOSA utilized the Eclipse Script Application Programming Interface (ESAPI) scripting language provided by Eclipse. Based on the input prescriptions, the algorithm simulates the operations of a physicist, automatically crops the target areas, and sets appropriate optimization parameters. Retrospectively, 20 cases of glioma and head and neck cancers were selected. Without considering organ-at-risk dose limits, dose calculations were performed using both ACOSA and Eclipse's built-in AutoCrop, and a dosimetric comparison was conducted.ResultsIn terms of target volume homogeneity index (HI) and D98, the AutoCrop group demonstrated slight superiority over the ACOSA group. However, the ACOSA group exhibited superior performance in conformity index (CI), gradient index (GI), D2, and particularly in parameters reflecting the rate of low-dose fall-off outside the target volume, including Ratio20, Ratio30, and Ratio40, when compared to the AutoCrop group.ConclusionsACOSA can be reliably applied in clinical settings and demonstrates superiority over the AutoCrop module of the Eclipse planning system.

IntroductionIdentifying therapeutic targets and early screening biomarkers is essential for improving the prognosis of lung cancer. CCT3 has been linked to tumor progression; however, its role in lung cancer proliferation and invasion, as well as its diagnostic significance remain poorly understood.MethodsCCT3 expression and its clinical correlations in lung cancer were analyzed utilizing data from the TCGA and GEO databases. The impact of CCT3 on cell proliferation, migration, and invasion was evaluated through CCK-8, colony formation, and Transwell assays. Western blotting was employed to assess the regulation of the PI3 K/AKT pathway and markers associate with epithelial-mesenchymal transition (EMT). Serum CCT3 levels in 714 participants were measured via ELISA, with diagnostic efficacy analyzed using receiver operating characteristic (ROC) curve analysis.ResultsCCT3 was over-expressed in lung cancer tissues, which was correlated with the stage of non-small lung cancer (NSCLC). CCT3 promotes cell proliferation, migration, and invasion by activating the PI3 K/AKT pathway and modulating EMT. In vivo, CCT3 knockdown significantly suppressed tumor growth in xenograft models. Elevated serum levels of CCT3 have been observed in patients with lung cancer, exhibiting high diagnostic efficacy for distinguishing NSCLC from benign nodules (AUC=0.873) and enhancing performance for small cell lung cancer when combined with proGRP.ConclusionCCT3 facilitates the progression of lung cancer through the PI3 K/AKT-EMT axis, positioning it as a potential therapeutic target and biomarker.

IntroductionAbnormal tumor vasculature impairs oxygen delivery and induces hypoxia, contributing to treatment resistance and poor prognosis in non-small cell lung cancer (NSCLC). Although radiation therapy can modulate tumor vessels, its effects vary widely due to vascular heterogeneity. Therefore, a reliable and noninvasive method to quantify vascular abnormality is needed to better predict treatment outcomes.MethodsWe developed a deep learning-based imaging biomarker, the Vessel Risk Score (VRS), to quantify tumor vascular abnormality from contrast-enhanced CT scans. Trained on multi-institutional data from 126 NSCLC patients treated with hypofractionated radiotherapy, the model learned vascular morphology patterns from tumor-vessel images. Using these learned patterns, vascular heterogeneity was quantified as the distributional difference from normal vessel morphology. The generalizability of VRS was then evaluated in an external cohort of 128 early-stage NSCLC patients who underwent stereotactic body radiotherapy (SBRT).ResultsVRS showed significantly better prediction of SBRT radiation therapy response compared to vessel density. The VRS of the responder group was 0.494 (95% CI: 0.47-0.52), significantly lower than the non-responder group's 0.578 (95% CI: 0.54-0.62). Additionally, patients with high VRS showed significantly shorter PFS compared to those with low VRS (p < 0.05). In Cox multivariate analysis, VRS emerged as the only significant predictor among vessel density and other clinical variables (p < 0.05).ConclusionThe proposed AI-derived VRS provides a noninvasive and reproducible measure of tumor vascular abnormality, offering improved prediction of radiation therapy response and prognosis compared with vessel density. This approach may extend to prognostic assessment in other cancer types where vascular morphology plays a critical role.