Technology in Cancer Research & Treatment最新文献

Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and treatment strategies for ccRCC. This study aims to create a pathomics model to predict OS in ccRCC patients. In this study, data from ccRCC patients in the TCGA database were used as a training set, with clinical data serving as a validation set. Pathological features were extracted from H&E-stained slides using PyRadiomics, and a pathomics model was constructed using the non-negative matrix factorization (NMF) algorithm. The model's predictive performance was assessed through Kaplan-Meier (KM) survival curves and Cox regression analysis. Additionally, differential gene expression, gene ontology (GO) enrichment analysis, immune infiltration, and mutational analysis were conducted to investigate the underlying biological mechanisms. A total of 368 pathomics features were extracted from H&E-stained slides of ccRCC patients, and a pathomics model comprising two subtypes (Cluster 1 and Cluster 2) was successfully constructed using the NMF algorithm. KM survival curves and Cox regression analysis revealed that Cluster 2 was associated with worse OS. A total of 76 differential genes were identified between the two subtypes, primarily involving extracellular matrix organization and structure. Immune-related genes, including CTLA4, CD80, and TIGIT, were highly expressed in Cluster 2, while the VHL and PBRM1 genes, along with mutations in the PI3K-Akt, HIF-1, and MAPK signaling pathways, exhibited mutation rates exceeding 40% in both subtypes. The machine learning-based pathomics model effectively predicts the OS of ccRCC patients and differentiates between subtypes. The critical roles of the immune-related gene CTLA4 and the PI3K-Akt, HIF-1, and MAPK signaling pathways offer new insights for further research on the molecular mechanisms, diagnosis, and treatment strategies for ccRCC.

This review article aims to synthesize existing data on radiation-induced heart diseases in patients undergoing chest radiation therapy and also explores cardiac-sparing techniques to mitigate cardiotoxic effects. We conducted a comprehensive database search to review and consolidate data regarding chest radiotherapy and effects on the heart as well as techniques to minimize exposure to the heart. The research findings demonstrate associations between radiation exposure to cardiac substructures and subsequent cardiotoxicity. This review also stresses the importance of identifying patients at high-risk for cardiotoxicity as well as advocates for the adoption of stringent cardiac dose constraints in these patients. Advanced cardiac-sparing techniques, notably respiratory motion management, have emerged as pivotal strategies to minimize the likelihood of cardiac events. This narrative review emphasizes the critical role of these innovations in optimizing cardiac health during radiation treatment.

Background: Immune checkpoint inhibitor (ICI) plus chemotherapy is effective in advanced gastric or gastroesophageal junction (G/GEJ) cancer. This study aims to evaluate the clinical effect of first-line immunotherapy in combination with chemotherapy for advanced G/GEJ cancer. Methods: PubMed, Web of Science, Embase and Cochrane databases were systematically searched from the inception of the databases to December 2021. Randomized trials comparing ICI plus chemotherapy with chemotherapy in first-line treatment for advanced G/GEJ cancer were included. The outcomes were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). Analyses were performed in Stata 14.0 software. The study protocol was registered with PROSPERO, number CRD42022300907. Results: Five trials were included for analysis, involving 2, 814 patients. ICI plus chemotherapy can significantly improve OS (hazards ratio [HR], 0.86; 95% CI 0.78-0.94; P = .002), PFS (HR, 0.79; 95% CI 0.63-0.99; P ＜ .001) and ORR (relative ratio [RR], 1.20; 95% CI 1.11-1.30; P < .001). In safety analyses, there were no significant differences in incidence of all AEs, treatment-related adverse event (TRAE), TRAE of grade 3 or higher, serious TRAE and TRAE leading to death between two arms (P > .05). Conclusions: ICI plus chemotherapy is more effective first-line treatment for advanced G/GEJ cancer in contrast to chemotherapy regrading to improving OS, PFS and ORR, without increasing TRAE risk. This study will redefine the role of ICI in combination with chemotherapy in the first-line setting for G/GEJ cancer, and provide reference for clinical treatment.

Introduction: This study explored the clinical value and application of ultrasound contrast imaging technology in the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) TR4 benign and malignant thyroid nodules.

Methods: We retrospectively analyzed data from the medical records of 40 patients who met the inclusion criteria between January 2020 and December 2023. Each patient was evaluated using the ACR TI-RADS classification and contrast-enhanced ultrasound (CEUS). The agreement between pathological outcomes and ultrasound indicators and the diagnostic value and significance of each parameter were assessed.

Results: The diameters did not differ between benign and malignant nodules (P = 0.324). Ring enhancement was closely associated with benign thyroid nodules, with a negative predictive value of 100%. Homogeneous enhancement and enhancement intensity showed good diagnostic value for pathological results, with an area under the curve (AUC) > 0.8. This parameter showed a high diagnostic value for serial and parallel combinations of homogeneous enhancement and enhancement intensity, with a sensitivity of 77.8% and specificity of 85.7% for the serial combination and 100% and 71.4%, respectively. for the parallel combination.

Conclusion: Among ACR TI-RADS TR4 nodules, diameter 1.0-1.5 cm was not significantly correlated with a benign or malignant nature. Nodules featuring ring enhancement with ring-enhancing features should be considered benign. Similarly, nodules showing no, homogeneous, or high enhancement with clear borders on CEUS imaging may be benign. However, nodules with uneven low enhancement or unclear borders may be malignant. Therefore, uneven and low enhancement on CEUS imaging may have a high diagnostic value for malignant nodules. Moreover, the combination of these features may have even higher specificity.

Objectives: This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice.

Methods: Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.

Results: Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive.

Conclusion: For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.

Conclusion: These findings indicate that EVs obtained from lung adenocarcinoma cells cultured under IH deliver miR-20a-5p to promote M2 macrophage polarization by targeting PTEN.

Purpose: PIWI-interacting RNAs (piRNAs) are a type of noncoding small RNA that can interact with PIWI-like RNA-mediated gene silencing (PIWIL) proteins to affect biological processes such as transposon silencing through epigenetic effects. Recent studies have found that piRNAs are widely dysregulated in tumors and associated with tumor progression and a poor prognosis. Therefore, this study aimed to investigate the effect of piR-1919609 on the proliferation, apoptosis, and drug resistance of ovarian cancer cells.

Methods: In this study, we used small RNA sequencing to screen and identify differentially expressed piRNAs in primary ovarian cancer, recurrent ovarian cancer, and normal ovaries. A large-scale verification study was performed to verify the expression of piR-1919609 in different types of ovarian tissue, including ovarian cancer tissue and normal ovaries, by RT-PCR and to analyze its association with the clinical prognosis of ovarian cancer. The expression of PIWILs in ovarian cancer was verified by RT-PCR, Western blotting and immunofluorescence. The effects of piR-1919609 on ovarian cancer cell proliferation, apoptosis and drug resistance were studied through in vitro and in vivo models.

Results: (1) piR-1919609 was highly expressed in platinum-resistant ovarian cancer tissues (p < 0.05), and this upregulation was significantly associated with a poor prognosis and a shorter recurrence time in ovarian cancer patients (p < 0.05). (2) PIWIL2 was strongly expressed in ovarian cancer tissues (p < 0.05). It was expressed both in the cytoplasm and nucleus of ovarian cancer cells. (3) Overexpression of piR-1919609 promoted ovarian cancer cell proliferation, inhibited apoptosis, and promoted tumor growth in nude mice. (4) Inhibition of piR-1919609 effectively reversed ovarian cancer drug resistance.

Conclusion: In summary, we showed that piR-1919609 is involved in the regulation of drug resistance in ovarian cancer cells and might be an ideal potential target for reversing platinum resistance in ovarian cancer.

Background: A clinical challenge in esophageal squamous cell carcinoma (ESCC) remains the lack of applicable plasma biomarkers for screening and diagnosis. Circular RNAs (circRNAs) hold great potential as biomarkers for cancer. The study aims to explore a circRNA as a potential plasma biomarker for screening strategies and diagnostic approaches to ESCC.

Methods: Upregulated circRNAs were identified through RNA sequencing, with circCYP24A1 being identified as the target circRNA. Fluorescence in situ hybridization was employed to detect the expression of circCYP24A1 in ESCC tissue microarrays, aiming to assess the expression of circCYP24A1 in a large population and its correlation with clinical indicators. Subsequently, qRT-PCR analysis was performed on plasma samples from both ESCC patients and healthy controls to evaluate the expression levels of circCYP24A1, exploring its potential as a biomarker. Finally, the functions of circCYP24A1 were validated through CCK-8 assay, wound healing assay, trans-well assays and western blot assays.

Results: CircCYP24A1 demonstrated upregulation in both plasma and tissues, exhibiting correlations with lymph node metastasis, TNM staging, and prognosis in ESCC. The circCYP24A1 achieved a perfect area under the curve of 0.94 for the diagnosis of ESCC, and an area under the curve of 0.76 for the prediction of lymph node metastasis. Furthermore, functional loss assays revealed that circCYP24A1 effectively promotes the epithelial-mesenchymal transition and tumor metastasis in vitro.

Conclusions: CircCYP24A1 emerges as a potential plasma diagnostic biomarker and a predictive factor for LNM for ESCC.

Tumor growth and metastasis rely on angiogenesis. In recent years, long non-coding RNAs have been shown to play an important role in regulating tumor angiogenesis. Here, we review the multidimensional modes and relevant molecular mechanisms of long non-coding RNAs in regulating tumor angiogenesis. In addition, we summarize new strategies for tumor anti-angiogenesis therapies by targeting long non-coding RNAs. The aim of this study is to provide new diagnostic targets and treatment strategies for anti-angiogenic tumor therapy.