The prognostic value of tumor-associated macrophages detected by immunostaining in diffuse large B cell lymphoma: A meta-analysis

BackgroundThe prognostic implication of tumor-associated macrophages (TAMs) in the microenvironment of diffuse large B cell lymphoma (DLBCL) remains controversial.MethodsA systematic and comprehensive search of relevant studies was performed in PubMed, Embase and Web of Science databases. The quality of the included studies was estimated using Newcastle-Ottawa Scale (NOS).ResultsTwenty-three studies containing a total of 2992 DLBCL patients were involved in this study. They were all high-quality studies scoring ≥ 6 points. High density of M2 TAMs in tumor microenvironment significantly associated with both advanced disease stage (OR= 1.937, 95% CI: 1.256-2.988, P = 0.003) and unfavorable overall survival (OS) (HR = 1.750, 95% CI: 1.188-2.579, P = 0.005) but not associated with poor progression free survival (PFS) (HR = 1.672, 95% CI: 0.864-3.237, P = 0.127) and international prognostic index (IPI) (OR= 1.705, 95% CI: 0.843-3.449, P = 0.138) in DLBCL patients. No significant correlation was observed between the density of CD68+ TAMs and disease stage (OR= 1.433, 95% CI: 0.656-3.130, P = 0.366), IPI (OR= 1.391, 95% CI: 0.573-3.379, P = 0.466), OS (HR=0.929, 95% CI: 0.607-1.422, P = 0.734) or PFS (HR= 0.756, 95% CI: 0.415-1.379, P = 0.362) in DLBCL patients.ConclusionThis meta-analysis demonstrated that high density of M2 TAMs in the tumor microenvironment was a robust predictor of adverse outcome for DLBCL patients.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42022343045

Construction and verification of prognostic nomogram for early-onset esophageal cancer

This study aimed to build up nomogram models to evaluate overall survival (OS) and cancer-specific survival (CSS) in early-onset esophageal cancer (EOEC). Patients diagnosed with esophageal cancer (EC) from 2004 to 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Clinicopathological characteristics of younger versus older patients were compared, and survival analysis was performed in both groups. Independent related factors influencing the prognosis of EOEC were identified by univariate and multivariate Cox analysis, which were incorporated to construct a nomogram. The predictive capability of the nomogram was estimated by the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). A total of 534 younger and 17,243 older patients were available from the SEER database. Younger patients were randomly segmented into a training set (n = 266) and a validation set (n = 268). In terms of the training set, the C-index of the OS nomogram was 0.740 (95% CI: 0.707-0.773), and that of the CSS nomogram was 0.752 (95% CI: 0.719-0.785). In view of the validation set, the C-index of OS and CSS were 0.706 (95% CI: 0.671-0.741) and 0.723 (95% CI: 0.690-0.756), respectively. Calibration curves demonstrated the consistent degree of fit between actual and predicted values in nomogram models. From the perspective of DCA, the nomogram models were more beneficial than the tumor-node-metastasis (TNM) and the SEER stage for EOEC. In brief, the nomogram model can be considered as an individualized quantitative tool to predict the prognosis of EOEC patients to assist clinicians in making treatment decisions

Prognostic and clinicopathological value of Beclin-1 expression in hepatocellular carcinoma: a meta-analysis

Abstract Background The abnormal expression of Beclin-1 has recently been investigated in a variety of tumors. However, previous studies have obtained contradicting results regarding the clinical and prognostic value of Beclin-1 in hepatocellular carcinoma (HCC). We performed a meta-analysis to clarify the prognostic value of Beclin-1 and its correlations with clinical pathological parameters in HCC. Methods Relevant studies were systematically retrieved from PubMed, EMBASE, China National Knowledge Infrastructure (CNKI), Wan Fang and Chinese VIP databases. We used the Newcastle-Ottawa scale (NOS) to estimate the quality of the involved studies. Results Ten eligible studies with 1086 HCC patients were included in this study. Our results showed that decreased Beclin-1 expression in HCC related to histological grade [poor-undifferentiated vs. well-moderate: odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.65–3.32, P < 0.00001]. The pooled hazard ratio (HR) (HR = 1.43, 95% CI = 1.17–1.75, P = 0.0004) indicated that decreased Beclin-1 expression correlated with poor overall survival (OS). Conclusions This meta-analysis indicated that decreased Beclin-1 expression might relate to poor differentiation and unfavorable outcome in HCC

Seven Fatty Acid Metabolism-Related Genes as Potential Biomarkers for Predicting the Prognosis and Immunotherapy Responses in Patients with Esophageal Cancer

Background: Esophageal cancer (ESCA) is a major cause of cancer-related mortality worldwide. Altered fatty acid metabolism is a hallmark of cancer. However, studies on the roles of fatty acid metabolism-related genes (FRGs) in ESCA remain limited. Method: We identified differentially expressed FRGs (DE-FRGs). Then, the DE-FRGs prognostic model was constructed and validated using a comprehensive analysis. Moreover, the correlation between the risk model and clinical characteristics was investigated. A nomogram for predicting survival was established and evaluated. Subsequently, the difference in tumor microenvironment (TME) was compared between two risk groups. The sensitivity of key DE-FRGs to chemotherapeutic interventions and their correlation with immune cells were investigated. Finally, DEGs between two risk groups were measured and the prognostic value of key DE-FRGs in ESCA was confirmed in other databases. Results: A prognostic model was constructed based on seven selected DEG-FRGs. TNM staging and CD8+ T cells were significantly correlated with high-risk groups. Low-risk groups exhibited more infiltrated M0 macrophages, an activation of type II interferon (IFN-&gamma;) responses, and were found to be more suitable for immunotherapy. Seven key DE-FRGs with prognostic value were found to be considerably influenced by different chemotherapy drugs. Conclusion: A prognostic model based on seven DE-FRGs may efficiently predict patient prognosis and immunotherapy response, helping to develop individualized treatment strategies in ESCA

miR-4443 promotes radiation resistance of esophageal squamous cell carcinoma via targeting PTPRJ

Abstract Background Radiotherapy is one of the main treatments for esophageal squamous cell carcinoma (ESCC), but its efficacy is limited by radioresistance. MicroRNAs play a crucial role in posttranscriptional regulation, which is linked to the cancer response to radiation. Methods We successfully established a radioresistant cell line model by using fractionated irradiation. qRT-PCR was adopted to detect the expression of miR-4443 in human normal esophageal cell lines, tumor cells, and radioresistant cells. Next, CCK-8, colony formation, apoptosis, and cell cycle assays were used to assess the biological effect of miR-4443. Weighted gene coexpression network analysis (WGCNA) was performed to identify potential radiosensitivity-related genes. Additionally, we predicted the probable targets of the miRNA using bioinformatic methods and confirmed them using Western blot. Results miR-4443 was significantly upregulated in radioresistant ESCC cells. Enhancement of miR-4443 further decreased the radiosensitivity of ESCC cells, while inhibition of miR-4443 increased the radiosensitivity of ESCC cells. Notably, miR-4443 modulated radiosensitivity by influencing DNA damage repair, apoptosis, and G2 cycle arrest. By using WGCNA and experimental validation, we identified PTPRJ as a key target for miRNA-4443 to regulate radiosensitivity. The effects of miR-4443 overexpression or inhibition could be reversed by increasing or decreasing PTPRJ expression. Conclusion In this study, miR-4443 is found to promote radiotherapy resistance in ESCC cells by regulating PTPRJ expression, which provides a new perspective and clue to alleviate radioresistance

Formation of Human Neuroblastoma in Mouse-Human Neural Crest Chimeras

© 2020 Neuroblastoma (NB), derived from the neural crest (NC), is the most common pediatric extracranial solid tumor. Here, we establish a platform that allows the study of human NBs in mouse-human NC chimeras. Chimeric mice were produced by injecting human NC cells carrying NB relevant oncogenes in utero into gastrulating mouse embryos. The mice developed tumors composed of a heterogenous cell population that resembled that seen in primary NBs of patients but were significantly different from homogeneous tumors formed in xenotransplantation models. The human tumors emerged in immunocompetent hosts and were extensively infiltrated by mouse cytotoxic T cells, reflecting a vigorous host anti-tumor immune response. However, the tumors blunted the immune response by inducing infiltration of regulatory T cells and expression of immune-suppressive molecules similar to escape mechanisms seen in human cancer patients. Thus, this experimental platform allows the study of human tumor initiation, progression, manifestation, and tumor-immune-system interactions in an animal model system

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated