Transarterial radioembolization (TARE) is a local radionuclide therapy and is successfully used in hepatocellular carcinoma (HCC) treatment. Radioactive microspheres have been widely studied for TARE. Preparation of ideal radioactive microspheres is significant for clinical research and patient treatment. In this study, we have designed a novel multifunctional microsphere, i.e., polydopamine (PDA)-coated 177Lu-radiolabeled silica microspheres (MS) denoted as 177Lu-MS@PDA, which can be used for TARE and photothermal therapy (PTT). The radiostability of 177Lu-MS@PDA was significantly improved by coating 177Lu-MS with PDA. In addition, the coating of PDA makes microspheres have excellent photothermal performance. MicroSPECT/CT images showed that 177Lu-MS@PDA was accurately embolized and remained in the tumor during the observation time. At the time, it also showed that 177Lu-MS@PDA was very stable in vivo. Furthermore, the anti-tumor results demonstrated that TARE combined with PTT of 177Lu-MS@PDA can significantly inhibit tumor growth without obvious side effects. 177Lu-MS@PDA holds great potential as a promising radioactive microsphere for HCC.
Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes’ (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC).Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model’s dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis.Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research.Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.
Hepatocellular carcinoma (HCC) is the sixth most commonly malignant tumor and the third leading cause of cancer-related death in the world, and the early diagnosis and treatment of patients with HCC is core in improving its prognosis. The early diagnosis of HCC depends largely on magnetic resonance imaging (MRI). MRI has good soft-tissue resolution, which is the international standard method for the diagnosis of HCC. However, MRI is still insufficient in the diagnosis of some early small HCCs and malignant nodules, resulting in false negative results. With the deepening of research on HCC, researchers have found many specific molecular biomarkers on the surface of HCC cells, which may assist in diagnosis and treatment. On the other hand, molecular imaging has progressed rapidly in recent years, especially in the field of cancer theranostics. Hence, the preparation of molecular imaging probes that can specifically target the biomarkers of HCC, combined with MRI testing in vivo, may achieve the theranostic purpose of HCC in the early stage. Therefore, in this review, taking MR imaging as the basic point, we summarized the recent progress regarding the molecular imaging targeting various types of biomarkers on the surface of HCC cells to improve the theranostic rate of HCC. Lastly, we discussed the existing obstacles and future prospects of developing molecular imaging probes as HCC theranostic nanoplatforms.
Background: Thyroid cancer is a common malignant tumor of the endocrine system that has shown increased incidence in recent decades. We explored the relationship between tumor-infiltrating immune cell classification and the prognosis of thyroid carcinoma.Methods: RNA-seq, SNV, copy number variance (CNV), and methylation data for thyroid cancer were downloaded from the TCGA dataset. ssGSEA was used to calculate pathway scores. Clustering was conducted using ConsensusClusterPlus. Immune infiltration was assessed using ESTIMATE and CIBERSORT. CNV and methylation were determined using GISTIC2 and the KNN algorithm. Immunotherapy was predicted based on TIDE analysis. Results: Three molecular subtypes (Immune-enrich(E), Stromal-enrich(E), and Immune-deprived(D)) were identified based on 15 pathways and the corresponding genes. Samples in Immune-E showed higher immune infiltration, while those in Immune-D showed increased tumor mutation burden (TMB) and mutations in tumor driver genes. Finally, Immune-E showed higher CDH1 methylation, higher progression-free survival (PFS), higher suitability for immunotherapy, and higher sensitivity to small-molecule chemotherapeutic drugs. Additionally, an immune score (IMScore) based on four genes was constructed, in which the low group showed better survival outcome, which was validated in 30 cancers. Compared to the TIDE score, the IMScore showed better predictive ability.Conclusion: This study constructed a prognostic evaluation model and molecular subtype system of immune-related genes to predict the thyroid cancer prognosis of patients. Moreover, the interaction network between immune genes may play a role by affecting the biological function of immune cells in the tumor microenvironment.
BackgroundGastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.MethodsBased on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.ResultsMonocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.ConclusionIn conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.
Background: Recent studies have shown that inflammatory indicators are closely related to the prognosis of patients with hepatocellular carcinoma, and they can serve as powerful indices for predicting recurrence and survival time after treatment. However, the predictive ability of inflammatory indicators has not been systematically studied in patients receiving transarterial chemoembolization (TACE). Therefore, the objective of this research was to determine the predictive value of preoperative inflammatory indicators for unresectable hepatocellular carcinoma treated with TACE. Methods: Our retrospective research involved 381 treatment-naïve patients in 3 institutions, including the First Affiliated Hospital of Soochow University, Nantong First People’s Hospital, and Nantong Tumor Hospital, from January 2007 to December 2020 that received TACE as initial treatment. Relevant data of patients were collected from the electronic medical record database, and the recurrence and survival time of patients after treatment were followed up. Least absolute shrinkage and selection operator (LASSO) algorithm was used to compress and screen the variables. We utilized Cox regression to determine the independent factors associated with patient outcomes and constructed a nomogram based on multivariate results. Finally, the nomogram was verified from discriminability, calibration ability, and practical applicability. Results: Multivariate analysis revealed that the levels of aspartate aminotransferase-to-platelet ratio index (APRI) and lymphocyte count were independent influential indicators for overall survival (OS), whereas the levels of platelet-to-lymphocyte ratio (PLR) was an independent influential index for progression. Nomograms exhibited an excellent concordance index (C-index), in the nomogram of OS, the C-index was 0.753 and 0.755 in training and validation cohort, respectively; and in the nomogram of progression, the C-index was 0.781 and 0.700, respectively. The time-dependent C-index, time-dependent receiver operating characteristic (ROC), and time-dependent area under the curve (AUC) of the nomogram all exhibited ideal discrimination ability. Calibration curves significantly coincided with the standard lines, which indicated that the nomogram had high stability and low degree of over-fitting. Decision curve analysis revealed a wider range of threshold probabilities and could augment net benefits. The Kaplan-Meier curves for risk stratification indicated that the prognosis of patients varied significantly between risk categories ( P < .0001). Conclusions: The developed prognostic nomograms based on preoperative inflammatory indicators revealed high predictive accuracy for survival and recurrence. It can be a valuable clinical instrument for guiding individualized treatment and predicting prognosis.
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