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Brain tumors are common solid pediatric malignancies and the main reason for cancer-related death in the pediatric setting. Recently, evidence has revealed that noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), play a critical role in brain tumor development and progression. Therefore, in this review article, we describe the functions and molecular mechanisms of ncRNAs in multiple types of cancer, including medulloblastoma, pilocytic astrocytoma, ependymoma, atypical teratoid/rhabdoid tumor, glioblastoma, diffuse intrinsic pontine glioma, and craniopharyngioma. We also mention the limitations of using ncRNAs as therapeutic targets because of the nonspecificity of ncRNA targets and the delivery methods of ncRNAs. Due to the critical role of ncRNAs in brain oncogenesis, targeting aberrantly expressed ncRNAs might be an effective strategy to improve the outcomes of pediatric patients with brain tumors., Graphical abstract, In this review article, the authors describe the functions and molecular mechanisms of ncRNAs, such as miRNAs, lncRNAs, and circRNAs, in pediatric brain malignancies, including medulloblastoma, pilocytic astrocytoma, ependymoma, atypical teratoid/rhabdoid tumor, glioblastoma, diffuse intrinsic pontine glioma, and craniopharyngioma.

Purpose Allicin, an extract of garlic, has antitumor effects in multiple tumor types. However, the efficacy of allicin for treating glioblastoma has not yet been examined. This study examined the antitumor effect of allicin on human cytomegalovirus (HCMV)-infected glioblastoma multiforme (GBM) and its role in cytokine signaling. Materials and methods HCMV-infected glioblastoma was modeled by transfection of U87MG glioblastoma cells with HMCV proteins. MTT assay was used to assess the effect of allicin on the proliferation of glioma cells. Western blot analysis was used to detect the effect of allicin on the expression of intermediate-early gene 2 (IE2) and p53. Reverse transcription-quantitative polymerase chain reaction was used to assess and the levels of interleukin (IL)-6 and interferon (IFN)-β. Single cell gel electrophoresis was used to analyze changes in radiotherapy-induced DNA damage. Results Transfection of the IE2 protein led to decreased p53 expression and increased glioblastoma cell proliferation. Allicin inhibited this proliferation in a dose- and time-dependent manner. An inhibitory effect on cytokine release was observed in GBM cells treated with allicin. After treatment with allicin, p53 levels increased significantly, whereas expression of the inflammatory factors such as IL-6 and IFN-β decreased. U87MG cells treated with allicin and 10 Gy irradiation had increased intracellular DNA damage compared to either treatment alone. Conclusion Allicin inhibited proliferation of glioblastoma cells in vitro. Allicin also inhibited cytokine release, upregulated p53 activity, and increased the sensitivity of glioblastoma to radiotherapy. These results suggest that allicin is effective against HCMV-infected glioblastomas.

Pancreatic ductal adenocarcinoma (PDAC) is the most frequent and aggressive pancreatic tumor characterized by high metastatic risk and special tumor microenvironment. To comprehensively delineate the complex intra-tumoral heterogeneity and the underlying mechanism during metastatic lesions malignant progression, single-cell RNA sequencing (scRNA-seq) was employed. PCA and TSNE were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each cluster. GSVA was employed to assign pathway activity estimates to individual cells. Lineage trajectory progression was inferred by monocle. CNV status was inferred to compare the heterogeneity among patients and subtypes by infercnv. Ligand-receptor interactions were identified by CellPhoneDB, and regulons network of cells was analyzed by SCENIC. Through RNA-sequencing of 6236 individual cells from 5 liver metastatic PDAC lesions, 10 major cell clusters are identified by using unbiased clustering analysis of expression profiling and well-known cell markers. Cells with high CNV level were considered as malignant cells and pathway analyses were carried out to highlight intratumor heterogeneity in PDAC. Pseudotime trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. The complex cellular communication suggested potential immunotherapeutic targets in PDAC. Regulon network identified multiple candidates for promising cell-specific transcriptional factors. Finally, metastatic-related genes expression levels and signaling pathways were validated in bulk RNA Sequencing data. This study contributed a comprehensive single-cell transcriptome atlas and contributed into novel insight of intratumor heterogeneity and molecular mechanism in metastatic PDAC.

Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking.Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing “maftools” package.Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA–miRNA–mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed.Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

Background: Accumulating evidence has supported that long non-coding ribonucleic acids (lncRNAs) could act as essential regulators in cancer immunity. An immune-related lncRNAs (irlncRNAs) risk signature without specific expression value was established to accurately predict prognosis in patients with breast cancer (BRCA).Methods: First, irlncRNAs were identified using co-expression analysis and differential expressed irlncRNAs (DEirlncRNAs) were recognized by “Limma” package. Then, DEirlncRNA pairs were determined using an iteration loop and a 0-or-1 screening matrix. Next, single factor test and Lasso algorithm followed by multivariate Cox regression were employed to establish risk signature. Besides, the Akaike information criterion (AIC) values were calculated to recognize the optimal cut-off point of low- or high-risk groups. Additionally, the potential role of risk score was explored in terms of overall survival, clinical variables, tumor immune microenvironment features, immunotherapeutic targets, and TMB (tumor mutation board) statues.Results: A total of 946 irlncRNAs were determined and 188 DEirlncRNAs were identified. 15 DEirlncRNA pairs were introduced into establishment of prognostic signature, which harbored powerful and independent predictive prognostic ability. Taking advantage of AIC values, risk model was demonstrated to accurately distinguish samples from the viewpoint of clinical outcome, clinicopathological features, infiltrating immune cells, and immunosuppressive biomarkers. Besides, comprehensive prognostic nomogram was constructed to quantitatively estimate risk. Finally, synergistic effect of risk score with TMB value was corroborated.Conclusions: The DEirlncRNA pairs risk signature without specific expression value possessed excellent prognostic performance and may provide direction for immunotherapy in BRCA.

BackgroundIncreasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.MethodsMultiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package.ResultsThree different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.ConclusionThis work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

Background: Liver cancer stem cells, characterized by self-renewal and initiating cancer cells, were decisive drivers of progression and therapeutic resistance in hepatocellular carcinoma (HCC). However, a comprehensive understanding of HCC stemness has not been identified.Methods: RNA sequencing information, corresponding clinical annotation, and mutation data of HCC were downloaded from The Cancer Genome Atlas-LIHC project. Two stemness indices, mRNA expression-based stemness index (mRNAsi) and epigenetically regulated-mRNAsi, were used to comprehensively analyze HCC stemness. Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data and single-sample gene-set enrichment analysis algorithm were performed to characterize the context of tumor immune microenvironment (TIME). Next, differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify significant mRNAsi-related modules with hub genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment pathways were analyzed to functionally annotate these key genes. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a prognostic signature. Kaplan–Meier survival curves and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS) were utilized to draw the landscape of TIME. Finally, the mutation data were analyzed by employing “maftools” package.Results: mRNAsi was significantly elevated in HCC samples. mRNAsi escalated as tumor grade increased, with poor prognosis presenting the higher stemness index. The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. Additionally, prognostic risk-clinical nomogram was drawn to estimate risk quantitatively. Moreover, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was elucidated.Conclusion: This work comprehensively elucidated that stemness characteristics served as a crucial player in clinical outcome, complexity of TIME, and immunotherapeutic prediction from both mRNAsi and mRNA level. Quantitative identification of stemness characteristics in individual tumor will contribute into predicting clinical outcome, mapping landscape of TIME further optimizing precision immunotherapy.

Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer.Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan–Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the “maftools” package to delineate the tumor mutation burden (TMB).Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5′-cytosine-phosphate-guanine-3′ (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)–related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status.Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Spatial topology rule is the primary method to insure the consistency and validity of spatial topology relation in GIS software. Topology rule can be divided into three categories according to geographic entity’s geometric shape: point topology rule, line topology rule and polygon topology rule. At first, this paper summarizes the various linear geographic entities’ topological relations which have practical application, then designs a series of linear entity topology rules detailedly. Based on these rules, this paper proposes a topology rule checking algorithm using quadtree, which is designed on the basis of MAPGIS7.4 spatial data model. The algorithm has already been applied to MAPGIS platform and gained good effects.