CD47 performs a vital function in cancer therapy by binding to different SIRPα, thrombospondin 1, and integrin. However, its role in tumor immunity and its correlation with prognosis among many cancer types remain unknown. The raw mRNA expression data of CD47 in cancer patients was downloaded from TCGA and GTEx datasets. The protein expression of CD47 was detected using a microarray. Kaplan Meier analysis and forest plot were performed to compare the effects of high and low expression of CD47 on overall survival in different cancers. In addition, the correlations between CD47 expression and immune cell infiltration, stromal components, immune checkpoint genes, tumor mutational burden (TMB), and microsatellite instability (MSI) were analyzed from the public database. The gene function was determined by Gene Set Enrichment Analysis (GSEA). The expressions of CD47 in CHOL, COAD, ESCA, HNSC, KIRC, STAD, and THCA were higher compared with normal tissues. Elevated expression of CD47 predicted poor prognosis in ACC, KICH, KIRP, LGG, PAAD and UCEC. CD47 expression was strongly associated with immune infiltrating cells among KICH, KIRP, LGG, and PAAD. In addition, significant positive correlations with most immune checkpoint genes including PDCD 1 (PD-1), CD274 (PD-L1), CTLA4 in BLCA, DLBC, KICH, KIRC, LUAD, LUSC, PAAD, PCPG, SKCM, STAD, UCEC, and UVM was noted for the expression of CD47. GSEA analysis demonstrated that CD47 was a key regulator in metabolism-related pathways. These findings provide novel evidence that CD47 could be utilized as a promising prognostic biomarker and combination treatment target in various cancers.

Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor’s histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics’ application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.

Objective: It is widely acknowledged that central nervous system (CNS) infection is a serious infectious disease accompanied by various complications. However, the accuracy of current detection methods is limited, leading to delayed diagnosis and treatment. In recent years, metagenomic next-generation sequencing (mNGS) has been increasingly adopted to improve the diagnostic yield. The present study sought to evaluate the value of mNGS in CNS infection diagnosis.Methods: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2022 guidelines, we searched relevant articles published in seven databases, including PubMed, Web of Science, and Cochrane Library, published from January 2014 to January 2022. High-quality articles related to mNGS applications in the CNS infection diagnosis were included. The comparison between mNGS and the gold standard of CNS infection, such as culture, PCR or serology, and microscopy, was conducted to obtain true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, which were extracted for sensitivity and specificity calculation.Results: A total of 272 related studies were retrieved and strictly selected according to the inclusion and exclusion criteria. Finally, 12 studies were included for meta-analysis and the pooled sensitivity was 77% (95% CI: 70–82%, I² = 39.69%) and specificity was 96% (95% CI: 93–98%, I² = 72.07%). Although no significant heterogeneity in sensitivity was observed, a sub-group analysis was conducted based on the pathogen, region, age, and sample pretreatment method to ascertain potential confounders. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for CNS infection was 0.91 (95% CI: 0.88–0.93). Besides, Deek's Funnel Plot Asymmetry Test indicated no publication bias in the included studies (Figure 3, p > 0.05).Conclusion: Overall, mNGS exhibits good sensitivity and specificity for diagnosing CNS infection and diagnostic performance during clinical application by assisting in identifying the pathogen. However, the efficacy remains inconsistent, warranting subsequent studies for further performance improvement during its clinical application.Study registration number: INPLASY202120002

Background: Natural killer (NK) cells-based therapies are one of the most promising strategies against cancer. The aim of this study is to investigate the natural killer cell related genes and its prognostic value in glioma. Methods: The Chinese Glioma Genome Atlas (CGGA) was used to develop the natural killer cell-related signature. Risk score was built by multivariate Cox proportional hazards model. A cohort of 326 glioma samples with whole transcriptome expression data from the CGGA database was included for discovery. The Cancer Genome Atlas (TCGA) datasets was used for validation. GO and KEGG were used to reveal the biological process and function associated with the natural killer cell-related signature. We also collected the clinical pathological features of patients with gliomas to analyze the association with tumor malignancy and patients’ survival. Results: We screened for NK-related genes to build a prognostic signature, and identified the risk score based on the signature. We found that NK-related risk score was independent of various clinical factors. Nature-killer cell gene expression is correlated with clinicopathological features of gliomas. Innovatively, we demonstrated the tight relation between the risk score and immune checkpoints, and found NK-related risk score combined with PD1/PDL1 patients could predict the patient outcome. Conclusion: Natural killer cell-related gene signature can predict malignancy of glioma and the survival of patients, these results might provide new view for the research of glioma malignancy and individual immunotherapy.

Ferroptosis is an iron-dependent form of programmed cell death induced by lipid peroxidation. This process is regulated by signaling pathways associated with redox balance, iron metabolism, and lipid metabolism. Cancer cells’ increased iron demand makes them especially susceptible to ferroptosis, significantly influencing cancer development, therapeutic response, and metastasis. Recent findings indicate that cancer cells can evade ferroptosis by downregulating key signaling pathways related to this process, contributing to drug resistance. This underscores the possibility of modulating ferroptosis as an approach to counteract drug resistance and enhance therapeutic efficacy. This review outlines the signaling pathways involved in ferroptosis and their interactions with cancer-related signaling pathways. We also highlight the current understanding of ferroptosis in cancer drug resistance, offering insights into how targeting ferroptosis can provide novel therapeutic approaches for drug-resistant cancers. Finally, we explore the potential of ferroptosis-inducing compounds and examine the challenges and opportunities for drug development in this evolving field.