Background A variety of regulatory approaches including immune modulation have been explored as approaches to either eradicate antitumor response or induce suppressive mechanism in the glioblastoma microenvironment. Thus, the study of immune-related long noncoding RNA (lncRNA) signature is of great value in the diagnosis, treatment, and prognosis of glioblastoma. Methods Glioblastoma samples with lncRNA sequencing and corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. Immune-lncRNAs co-expression networks were built to identify immune-related lncRNAs via Pearson correlation. Based on the median risk score acquired in the training set, we divided the samples into high- and low-risk groups and demonstrate the survival prediction ability of the immune-related lncRNA signature. Both principal component analysis (PCA) and gene set enrichment analysis (GSEA) were used for immune state analysis. Results A cohort of 151 glioblastoma samples and 730 immune-related genes were acquired in this study. A five immune-related lncRNA signature (AC046143.1, AC021054.1, AC080112.1, MIR222HG, and PRKCQ-AS1) was identified. Compared with patients in the high-risk group, patients in the low-risk group showed a longer overall survival (OS) in the training, validation, and entire TCGA set (p = 1.931e-05, p = 1.706e-02, and p = 3.397e-06, respectively). Additionally, the survival prediction ability of this lncRNA signature was independent of known clinical factors and molecular features. The area under the ROC curve (AUC) and stratified analyses were further performed to verify its optimal survival predictive potency.  https://www.selleckchem.com/products/ldc203974-imt1b.html  Of note, the high-and low-risk groups exhibited significantly distinct immune state according to the PCA and GSEA analyses. Conclusions Our study proposes that a five immune-related lncRNA signature can be utilized as a latent indicator of prognosis and potential therapeutic approach for glioblastoma.Background As a transcription factor, Zinc finger protein ZIC2 can interact with various DNAs and proteins. Current studies have shown that ZIC2 plays an oncogene role in various cancers. In this study, we systematically characterize the prevalence and predictive value of ZIC2 expression across multiple cancer types. Methods We mined several public databases, including Oncomine, the Cancer Genome Atlas (TCGA), cBioPortal, Kaplan-Meier Plotter and PrognoScan to evaluated the differentially expressed ZIC2 between tumor samples and normal control samples in pan-cancner, and then explored the association between ZIC2 expression and patient survival, prognosis and clinicopathologic stage. We also analyzed the relationship between tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, tumor- and immune-related genes and ZIC2 expression. Finally, we explored the potential signaling pathway mechanism through gene set enrichment analysis (GSEA). Results ZIC2 expression was higher in most cancer tissues compared with adjacent normal tissues. High ZIC2 expression was associated with worse prognosis and a higher clinicopathologic stage. ZIC2 expression was strongly associated with the TMB, MSI, tumor microenvironment and tumor- and immune-related genes. The GSEA revealed that multiple tumor- and immune-related pathways were differentially enriched in ZIC2 high or low expression phenotype. Conclusion ZIC2 expression may be a potential prognostic molecular biomarker of poor survival in pan-cancer and may act as an oncogene with a strong effect in the processes of tumorigenesis and progression.In mammalian cells, cell cycle entry occurs in response to the correct stimuli and is promoted by the transcriptional activity of E2F family members. E2F proteins regulate the transcription of S phase cyclins and genes required for DNA replication, DNA repair, and apoptosis. The activity of E2F1, the archetypal and most heavily studied E2F family member, is tightly controlled by the DNA damage checkpoints to modulate cell cycle progression and initiate programmed cell death, when required. Altered tumor suppressor and oncogenic signaling pathways often result in direct or indirect interference with E2F1 regulation to ensure higher rates of cell proliferation independently of external cues. Despite a clear link between dysregulated E2F1 activity and cancer progression, literature on the contribution of E2F1 to DNA replication stress phenotypes is somewhat scarce. This review discusses how dysfunctional tumor suppressor and oncogenic signaling pathways promote the disruption of E2F1 transcription and hence of its transcriptional targets, and how such events have the potential to drive DNA replication stress. In addition to the involvement of E2F1 upstream of DNA replication stress, this manuscript also considers the role of E2F1 as a downstream effector of the response to this type of cellular stress. Lastly, the review introduces some reflections on how E2F1 activity is integrated with checkpoint control through post-translational regulation, and proposes an exploitable tumor weakness based on this axis.Background With the growing uncovering of drug resistance in melanoma treatment, personalized cancer therapy and cancer stem cells are potential therapeutic targets for this aggressive skin cancer. Methods Multi-omics data of cutaneous melanoma were obtained from The Cancer Genome Atlas (TCGA) database. Then, these melanoma patients were classified into different subgroups by performing "CancerSubtypes" method. The differences of stemness indices (mRNAsi and mDNAsi) and tumor microenvironment indices (immune score, stromal score, and tumor purity) among subtypes were investigated. Moreover, the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms were performed to identify a cancer cell stemness feature, and the likelihood of immuno/chemotherapeutic response was further explored. Results Totally, 3 specific subtypes of melanoma with different survival outcomes were identified from TCGA. We found subtype 2 of melanoma with the higher immune score and stromal score and lower mRNAsi and tumor purity score, which has the best survival time than the other subtypes.