Technology in Cancer Research & Treatment最新文献

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system. RNA methylation plays an important role in tumorigenesis and metastasis, which could alter gene expression and even function at multiple levels, such as RNA splicing, stability, translocation, and translation. In this study, we aimed to conduct a comprehensive analysis of RNA methylation-related genes (RMGs) in HCC and their relationship with survival and clinical features.

Methods: A retrospective analysis was performed using publicly available HCC-related datasets. The differentially expressed genes (DEGs) between HCC and controls were identified from TCGA-LlHC and intersected with RMGs to obtain differentially expressed RNA methylation-related genes (DERMGs). Regression analysis was used to screen for prognostic genes and construct risk models. Simultaneously, clinical, immune infiltration and therapeutic efficacy analyses were performed. Finally, multivariate cox regression was used to identify independent risk factors, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression levels of the core genes of the model.

Results: A 21-gene risk model for HCC was established with excellent performance based on ROC curves and survival analysis. Risk scores correlated with tumor grade, pathologic T, and TNM stage. Immune infiltration analysis showed correlations with immune scores, 11 immune cells, and 30 immune checkpoints. Low-risk patients showed a higher susceptibility to immunotherapy. The risk score and TNM stage were independent prognostic factors. qRT-PCR confirmed higher expression of PRDM9, ALPP, and GAD1 in HCC.

Conclusions: This study identified RNA methylation-related signature genes in HCC and constructed a risk model that predicts patient outcomes and reflects the immune microenvironment. Prognostic genes are involved in complex regulatory mechanisms, which may be useful for cancer diagnosis, prognosis, and therapy.

Cancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.

Objective: This real-world study aimed to investigate the efficacy and safety of palbociclib plus endocrine therapy in patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer in the real world in a Chinese population.

Methods: The clinical data of consecutively enrolled patients from the Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, and the University of Hong Kong - Shenzhen Hospital were collected. Progression-free survival curves were generated using log-rank tests with the Kaplan-Meier method. Univariate and multivariate logistic regression analyses were performed to identify the factors affecting progression-free survival.

Results: In total, 118 patients were enrolled, including 6 patients with brain metastases. At the last follow-up date, the median progression-free survival was 16.8 months (95% confidence interval, 11.1-22.5), with the 6-month and 12-month progression-free survival rates of 77.1% and 57.6%, respectively. The disease control rate and the intracranial disease control rate were 82.2% and 50%, respectively. A longer progression-free survival was observed for patients with the following characteristics: treatment-naive; without hepatic metastasis; sensitive to previous endocrine therapy and harboring fewer metastatic sites. The multivariate logistic regression analysis demonstrated that treatment lines and exposure to palliative chemotherapy were independent influencing factors of progression-free survival.

Conclusions: Palbociclib plus endocrine therapy in patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer was effective and well-tolerated, even in patients with brain metastases. More benefits were observed in frontline therapy, chemotherapy-naive, and endocrine therapy-sensitive patients with fewer metastatic sites.

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca²⁺. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.

Proton radiotherapy may be a compelling technical option for the treatment of breast cancer due to its unique physical property known as the "Bragg peak." This feature offers distinct advantages, promising superior dose conformity within the tumor area and reduced radiation exposure to surrounding healthy tissues, enhancing the potential for better treatment outcomes. However, proton therapy is accompanied by inherent challenges, primarily higher costs and limited accessibility when compared to well-developed photon irradiation. Thus, in clinical practice, it is important for radiation oncologists to carefully select patients before recommendation of proton therapy to ensure the transformation of dosimetric benefits into tangible clinical benefits. Yet, the optimal indications for proton therapy in breast cancer patients remain uncertain. While there is no widely recognized methodology for patient selection, numerous attempts have been made in this direction. In this review, we intended to present an inspiring summarization and discussion about the current practices and exploration on the approaches of this treatment decision-making process in terms of treatment-related side-effects, tumor control, and cost-efficiency, including the normal tissue complication probability (NTCP) model, the tumor control probability (TCP) model, genomic biomarkers, cost-effectiveness analyses (CEAs), and so on. Additionally, we conducted an evaluation of the eligibility criteria in ongoing randomized controlled trials and analyzed their reference value in patient selection. We evaluated the pros and cons of various potential patient selection approaches and proposed possible directions for further optimization and exploration. In summary, while proton therapy holds significant promise in breast cancer treatment, its integration into clinical practice calls for a thoughtful, evidence-driven strategy. By continuously refining the patient selection criteria, we can harness the full potential of proton radiotherapy while ensuring maximum benefit for breast cancer patients.

Triple-negative breast cancer (TNBC) comprises a group of aggressive and heterogeneous breast carcinoma. Chemotherapy is the mainstay for the treatment of triple-negative tumors. Nevertheless, the success of chemotherapeutic treatments is limited by their toxicity and development of acquired resistance leading to therapeutic failure and tumor relapse. Hence, there is an urgent need to explore novel targeted therapies for TNBC. Receptor tyrosine kinases (RTKs) are a family of transmembrane receptors that are key regulators of intracellular signaling pathways controlling cell proliferation, differentiation, survival, and motility. Aberrant activity and/or expression of several types of RTKs have been strongly connected to tumorigenesis. RTKs are frequently overexpressed and/or deregulated in triple-negative breast tumors and are further associated with tumor progression and reduced survival in patients. Therefore, targeting RTKs could be an appealing therapeutic strategy for the treatment of TNBC. This review summarizes the current evidence regarding the antitumor activity of RTK inhibitors in preclinical models of TNBC. The review also provides insights into the clinical trials evaluating the use of RTK inhibitors for the treatment of patients with TNBC.

Objectives: The research's purpose is to develop a software that automatically integrates and overlay 3D virtual models of kidneys harboring renal masses into the Da Vinci robotic console, assisting surgeon during the intervention.

Introduction: Precision medicine, especially in the field of minimally-invasive partial nephrectomy, aims to use 3D virtual models as a guidance for augmented reality robotic procedures. However, the co-registration process of the virtual images over the real operative field is performed manually.

Methods: In this prospective study, two strategies for the automatic overlapping of the model over the real kidney were explored: the computer vision technology, leveraging the super-enhancement of the kidney allowed by the intraoperative injection of Indocyanine green for superimposition and the convolutional neural network technology, based on the processing of live images from the endoscope, after a training of the software on frames from prerecorded videos of the same surgery. The work-team, comprising a bioengineer, a software-developer and a surgeon, collaborated to create hyper-accuracy 3D models for automatic 3D-AR-guided RAPN. For each patient, demographic and clinical data were collected.

Results: Two groups (group A for the first technology with 12 patients and group B for the second technology with 8 patients) were defined. They showed comparable preoperative and post-operative characteristics. Concerning the first technology the average co-registration time was 7 (3-11) seconds while in the case of the second technology 11 (6-13) seconds. No major intraoperative or postoperative complications were recorded. There were no differences in terms of functional outcomes between the groups at every time-point considered.

Conclusion: The first technology allowed a successful anchoring of the 3D model to the kidney, despite minimal manual refinements. The second technology improved kidney automatic detection without relying on indocyanine injection, resulting in better organ boundaries identification during tests. Further studies are needed to confirm this preliminary evidence.

Background: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy.

Methods: We utilized the "limma" package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identified using the "WGCNA" package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a prognostic feature associated with palmitoylation and developed a prognostic nomogram with the "regplot" package. The predictive values of the model for chemotherapy and immunotherapy responses were assessed using immunophenoscore (IPS) and the "pRophetic" package.

Results: We identified 211 differentially expressed genes related to palmitoylation, among which 44 demonstrated prognostic potential. Subsequently, a predictive model comprising eleven palmitoylation-related genes was developed. Patients were classified into high-risk and low-risk groups based on the median risk score. The findings revealed that individuals in the high-risk group exhibited lower survival rates, while those in the low-risk group showed increased immune cell infiltration and improved responses to chemotherapy and immunotherapy. Moreover, the BC-Palmitoylation Tool website was established.

Conclusion: This study developed the first machine learning-based predictive model for palmitoylation-related genes and created a corresponding website, providing clinicians with a valuable tool to improve patient outcomes.

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus blocking it improves the clearance of tumor cells by the immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab (Opdivo^® from Bristol-Myers Squibb) and pembrolizumab (Keytruda^® from Merck), there are ongoing efforts to discover new and improved checkpoint inhibitor therapeutics. In this study, we present multiple anti-PD-1 antibody fragments that were derived computationally using protein diffusion and evaluated through our scalable, in silico pipeline. Here we present nine synthetic Fv structures that are suitable for further empirical testing of their anti-PD-1 activity due to desirable predicted binding performance.