Glioma is one of the most common primary malignancies of the central nervous system, which has aggressive clinical behavior and a poorer prognosis. MicroRNAs (miRs) are a class of small noncoding RNAs that function as mediators of gene expression, which can be sponged by circRNA provided with a closed circular structure. Dysregulations of circular RNAs (circRNAs) and miRs have been implicated in the development and progression of glioma. In the current study, we investigated the role of circular RNA hsa_circ_0076248 in mediating the oncogenesis of glioma by sponging miR‐181a to modulate silent information regulator 1 (SIRT1) expression in vitro and in vivo. The quantitative real‐time polymerase chain reaction results showed that the expression of miR‐181a was significantly decreased in glioma tissues and cell lines compared with normal brain tissues and normal gliocyte, respectively, and the expression of hsa_circ_0076248 and SIRT1 demonstrated the opposite. Bioinformatics analysis identified hsa_circ_0076248 could sponge miR‐181a, and miR‐181a could target the mRNA of SIRT1. Our results verified that downregulating hsa_circ_0076248 or upregulating miR‐181a could depress the proliferation and invasion of glioma in vitro and in vivo. The experiment also showed that downregulating hsa_circ_0076248 or upregulating miR‐181a could remarkably promote the temozolomide chemotherapy sensitivity. Furthermore, Western blot analysis testified that downregulating hsa_circ_0076248 or upregulating miR‐181a could promote the expression of p53 and SIRT1. In summary, our study sheds light on the regulatory mechanism of hsa_circ_0076248 in glioma growth and invasion via sponging miR‐181a, which downregulates the SIRT1 expression and also suggests that hsa_circ_0076248, miR‐181a, and SIRT1 may serve as potential therapeutic targets for glioma.

Armadillo repeat-containing proteins (ARMCs) are widely distributed in eukaryotes and have important influences on cell adhesion, signal transduction, mitochondrial function regulation, tumorigenesis, and other processes. These proteins share a similar domain consisting of tandem repeats approximately 42 amino acids in length, and this domain constitutes a substantial platform for the binding between ARMCs and other proteins. An ARMC subfamily, including ARMC1∼10, ARMC12, and ARMCX1∼6, has received increasing attention. These proteins may have many terminal regions and play a critical role in various diseases. On the one hand, based on their similar central domain of tandem repeats, this ARMC subfamily may function similarly to other ARMCs. On the other hand, the unique domains on their terminals may cause these proteins to have different functions. Here, we focus on the ARMC subfamily (ARMC1∼10, ARMC12, and ARMCX1∼6), which is relatively conserved in vertebrates and highly conserved in mammals, particularly primates. We review the structures, biological functions, evolutions, interactions, and related diseases of the ARMC subfamily, which involve more than 30 diseases and 40 bypasses, including interactions and relationships between more than 100 proteins and signaling molecules. We look forward to obtaining a clearer understanding of the ARMC subfamily to facilitate further in-depth research and treatment of related diseases.

Background: Lung cancer is the most aggressive cancer, resulting in one-quarter of all cancer-related deaths, and its metastatic spread accounts for >70% of these deaths, especially metastasis to the brain. Metastasis-associated mutations are important biomarkers for metastasis prediction and outcome improvement. Methods: In this study, we applied whole-exome sequencing (WES) to identify potential metastasis-related mutations in 12 paired lung cancer and brain metastasis samples. Results: We identified 1,702 single nucleotide variants (SNVs) and 6,131 mutation events among 1,220 genes. Furthermore, we identified several lung cancer metastases associated genes (KMT2C, AHNAK2). A mean of 3.1 driver gene mutation events per tumor with the dN/dS (non-synonymous substitution rate/synonymous substitution rate) of 2.13 indicating a significant enrichment for cancer driver gene mutations. Mutation spectrum analysis found lung-brain metastasis samples have a more similar Ti/Tv (transition/transversion) profile with brain cancer in which C to T transitions are more frequent while lung cancer has more C to A transversion. We also found the most important tumor onset and metastasis pathways, such as chronic myeloid leukemia, ErbB signaling pathway, and glioma pathway. Finally, we identified a significant survival associated mutation gene ERF in both The Cancer Genome Atlas (TCGA) (P=0.01) and our dataset (P=0.012). Conclusions: In summary, we conducted a pairwise lung-brain metastasis based exome-wide sequencing and identified some novel metastasis-related mutations which provided potential biomarkers for prognosis and targeted therapeutics.

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.

Purpose: Neuroinflammation occurs in response to central nervous system (CNS) injury, infection, stimulation by toxins, or autoimmunity. We previously analyzed the downstream molecular changes in HT22 cells (mouse hippocampal neurons) upon lipopolysaccharide (LPS) stimulation. We detected elevated expression of Fibrillarin (FBL), a nucleolar methyltransferase, but the associated proinflammatory mechanism was not systematically elucidated. The aim of this study was to investigate the underlying mechanisms by which FBL affects neuroinflammation. Methods: RT-real-time PCR, Western blotting and immunofluorescence were used to assess the mRNA and protein expression of FBL in HT22 cells stimulated with LPS, as well as the cellular localization and fluorescence intensity of FBL. BAY-293 (a son of sevenless homolog 1 (SOS1) inhibitor), SR11302 (an activator protein-1 (AP-1) inhibitor) and KRA-533 (a KRAS agonist) were used to determine the molecular mechanisms underlying the effect of FBL. AP-1 was predicted to be the target protein of FBL by molecular docking analysis, and validation was performed with T-5224 (an AP-1 inhibitor). In addition, the downstream signaling pathways of FBL were identified by transcriptome sequencing and verified by RT-real-time PCR. Results: LPS induced FBL mRNA and protein expression in HT22 cells. In-depth mechanistic studies revealed that when we inhibited c-Fos, AP-1, and SOS1, FBL expression decreased, whereas FBL expression increased when KRAS agonists were used. In addition, the transcript levels of inflammatory genes in the NF-kB signaling pathway (including CD14, MYD88, TNF, TRADD, and NFKB1) were elevated after the overexpression of FBL. Conclusion: LPS induced the expression of FBL in HT22 cells through the RAS/MAPK signaling pathway, and FBL further activated the NF-kB signaling pathway, which promoted the expression of relevant inflammatory genes and the release of cytokines. The present study reveals the mechanism by which FBL promotes neuroinflammation and offers a potential target for the treatment of neuroinflammation.

The seminal contributions of Jenner and Pasteur marked a paradigm shift in the field, transitioning immunology from the realm of mystical experience to that of reproducible science. The foundational principle underlying this transition, namely the utilization of pathogen characteristics to stimulate specific bodily defences, continues to serve as a foundational principle in contemporary immunotherapy. In this review, commencing with the origins and development of immunotherapy, we propose a “synergistic functional loop” model of tumor immunity, which comprises an antigen-sensing loop, a cytotoxic-killing loop, an immunoregulatory loop, and a tumor-educating loop. The dynamic equilibrium between these loops is pivotal in determining the ultimate anti-tumor effect. We propose a hierarchical efficacy pyramid for tumor immunotherapy, delineated by the biological depth of the immune response, which comprises three levels: local microenvironmental remodeling, systemic immune activation, and precise cell killing. A systematic comparison of the efficacy, safety, degree of individualization, engineering potential, and combination therapy potential of various immunotherapeutic techniques is conducted. The present analysis draws from successful and failed clinical trials, offering a comprehensive and nuanced perspective on the landscape of immunotherapy. A review of successful clinical trials indicates that effective immunotherapy must take into account several factors, including the type of tumor, the expression of molecular markers, the immune microenvironment, the patient’s immune status, and treatment history. Conversely, the failure of clinical trials highlights significant challenges, including the complexity of mechanisms, tumor heterogeneity, immunosuppression and immune escape, and drug resistance. The current challenges are elucidated, and novel perspectives on addressing these issues are proposed.