Glioma groups, including lower-grade glioma (LGG) and glioblastoma multiforme (GBM), are the most common primary brain tumor. Malignant gliomas, especially glioblastomas, are associated with a dismal prognosis. Hypoxia is a driver of the malignant phenotype in glioma groups; it triggers a cascade of immunosuppressive processes and malignant cellular responses (tumor progression, anti-apoptosis, and resistance to chemoradiotherapy), which result in disease progression and poor prognosis. However, approaches to determine the extent of hypoxia in the tumor microenvironment are still unclear. Here, we downloaded 575 LGG patients and 354 GBM patients from Chinese Glioma Genome Atlas (GGGA), and 530 LGG patients and 167 GBM patients from The Cancer Genome Atlas (TCGA) with RNA sequence and clinicopathological data. We developed a hypoxia risk model to reflect the immune microenvironment in glioma and predict prognosis. High hypoxia risk score was associated with poor prognosis and indicated an immunosuppressive microenvironment. Hypoxia signature significantly correlated with clinical and molecular features and could serve as an independent prognostic factor for glioma patients. Moreover, Gene Set Enrichment Analysis showed that gene sets associated with the high-risk group were involved in carcinogenesis and immunosuppression signaling. In conclusion, we developed and validated a hypoxia risk model, which served as an independent prognostic indicator and reflected overall immune response intensity in the glioma microenvironment.

Rationale: Accumulating evidence supports the importance of radiation therapy in the induction of antitumor immunity. Small extracellular vesicles (sEVs) play essential roles in tumor antigen loading and delivery. However, the role of sEVs in radiation-induced antitumor immunity remains unclear. It is therefore important to determine the role and regulatory mechanisms of sEVs in radiation-induced immunity.

Secreted phosphoprotein 1 (SPP1) shows carcinogenic potential in multiple cancers, yet its role in nasopharyngeal carcinoma (NPC) remains elusive. Leveraging transcriptomic data sourced from an NPC cohort at Fujian Cancer Hospital, alongside datasets from the Gene Expression Omnibus cohort and a single-cell RNA sequencing dataset, this investigation explored the role of SPP1 in tumor progression and the tumor microenvironment of NPC. A co-culture system involving tumor cells and macrophages was established to elucidate the relationship between SPP1 and tumor-associated macrophages in NPC. Subsequently, we established an SPP1-driven M2 macrophage signature using a machine-learning-based framework to predict patient prognosis. The results of our analysis indicated that SPP1 is associated with an elevated risk of disease progression and poor prognosis in NPC. Single-cell analysis demonstrated that SPP1 is a pivotal gene in the polarization of M2 macrophages within the tumor microenvironment. In vitro experiments demonstrated that NPC-derived SPP1 has the potential to activate the CD44/JAK2/STAT3 signaling pathway, promoting macrophage recruitment and polarization of the M2 subtype. Furthermore, we established a comprehensive SPP1-related M2 macrophage signature that can predict the prognosis and immune characteristics of patients with NPC. Our findings offer new insights into the role of SPP1 in the tumor microenvironment of NPC, and provide a novel SPP1-driven M2 macrophage signature with the potential to serve as a valuable tool for prognosis prediction and personalized therapy in NPC.

Laser tweezer Raman spectroscopy combined with multivariate statistical analysis was used for evaluating the radiotherapeutic effect on a single tumor cell.

Although radiation therapy (RT) plays a critical role in the treatment of low-grade glioma (LGG), many patients suffer from adverse effects without experiencing survival benefits. In various carcinomas, long non-coding RNAs (lncRNAs) contribute to pathogenic processes, including tumorigenesis, metastasis, chemoresistance, and radioresistance. Currently, the role of lncRNAs in the radiosensitivity of LGG is largely unknown. Here, we downloaded clinical data for 167 LGG patients from The Cancer Genome Atlas database and divided them between radiosensitive and radioresistant groups based on their clinical outcomes after receiving radiotherapy. We identified 37 lncRNAs that were differentially expressed (DElncRNAs) between the groups. Functional enrichment analysis revealed that their potential target mRNAs were mainly enriched in the PI3K-Akt and MAPK signaling pathways and in DNA damage response. Kaplan-Meier survival analysis revealed that increased expression of six lncRNAs was significantly associated with radiosensitivity. We then developed a risk signature based on three of the DElncRNAs that served as an independent biomarker for predicting LGG patient outcomes after radiotherapy. In vitro experiments further validated the biological function of these lncRNAs on low-grade glioma radiation response.