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In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.

microRNA profiles of anaplastic lymphoma kinase ( ALK )-driven non-small cell lung cancers (NSCLCs) are currently not available in publically accessible databases. Identifying translocated ALK , mutant EGF receptor, and mutant V-Ki-ras2 Kirsten rat sarcoma cases in NSCLC is of value for determining which patients are more likely to benefit from a targeted therapy, to explicate mechanisms underlying chemotherapy survival, and ultimately in new drug development. microRNA-based classifiers are newly developed prognostic and diagnostic tools that can improve and complement the current gold-standard techniques. These classifiers also potentially represent an engine for boosting research on the role of these microRNAs in response to commonly used chemotherapy regimens in NSCLC to maximize patient outcomes. microRNAs (miRNAs) can act as oncosuppressors or oncogenes, induce chemoresistance or chemosensitivity, and are major posttranscriptional gene regulators. Anaplastic lymphoma kinase ( ALK ), EGF receptor ( EGFR ), and V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog ( KRAS ) are major drivers of non-small cell lung cancer (NSCLC). The aim of this study was to assess the miRNA profiles of NSCLCs driven by translocated ALK , mutant EGFR , or mutant KRAS to find driver-specific diagnostic and prognostic miRNA signatures. A total of 85 formalin-fixed, paraffin-embedded samples were considered: 67 primary NSCLCs and 18 matched normal lung tissues. Of the 67 primary NSCLCs, 17 were echinoderm microtubule-associated protein-like 4– ALK translocated ( ALK + ) lung cancers; the remaining 50 were not ( ALK − ). Of the 50 ALK − primary NSCLCs, 24 were EGFR and KRAS mutation-negative (i.e., WT; triple negative); 11 were mutant EGFR ( EGFR + ), and 15 were mutant KRAS ( KRAS + ). We developed a diagnostic classifier that shows how miR-1253, miR-504, and miR-26a-5p expression levels can classify NSCLCs as ALK -translocated, mutant EGFR , or mutant KRAS versus mutation-free. We also generated a prognostic classifier based on miR-769-5p and Let-7d-5p expression levels that can predict overall survival. This classifier showed better performance than the commonly used classifiers based on mutational status. Although it has several limitations, this study shows that miRNA signatures and classifiers have great potential as powerful, cost-effective next-generation tools to improve and complement current genetic tests. Further studies of these miRNAs can help define their roles in NSCLC biology and in identifying best-performing chemotherapy regimens.

Breast cancer (BC) is the most frequent malignancy identified in adult females, resulting in enormous financial losses worldwide. Owing to the heterogeneity as well as various molecular subtypes, the molecular pathways underlying carcinogenesis in various forms of BC are distinct. Therefore, the advancement of alternative therapy is required to combat the ailment. Recent analyses propose that long non-coding RNAs (lncRNAs) perform an essential function in controlling immune response, and therefore, may provide essential information about the disorder. However, their function in patients with triple-negative BC (TNBC) has not been explored in detail. Here, we analyzed the changes in the genomic expression of messenger RNA (mRNA) and lncRNA in standard control in response to cancer metastasis using publicly available single-cell RNA-Seq data. We identified a total of 197 potentially novel lncRNAs in TNBC patients of which 86 were differentially upregulated and 111 were differentially downregulated. In addition, among the 909 candidate lncRNA transcripts, 19 were significantly differentially expressed (DE) of which three were upregulated and 16 were downregulated. On the other hand, 1901 mRNA transcripts were significantly DE of which 1110 were upregulated and 791 were downregulated by TNBCs subtypes. The Gene Ontology (GO) analyses showed that some of the host genes were enriched in various biological, molecular, and cellular functions. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that some of the genes were involved in only one pathway of prostate cancer. The lncRNA-miRNA-gene network analysis showed that the lncRNAs TCONS_00076394 and TCONS_00051377 interacted with breast cancer-related micro RNAs (miRNAs) and the host genes of these lncRNAs were also functionally related to breast cancer. Thus, this study provides novel lncRNAs as potential biomarkers for the therapeutic intervention of this cancer subtype.

Hypoxia and stemness are important factors in tumor progression. We aimed to explore the ncRNA classifier associated with hypoxia and stemness in lung adenocarcinoma (LUAD). We found that the prognosis of LUAD patients with high hypoxia and stemness index was worse than that of patients with low hypoxia and stemness index. RNA expression profiles of these two clusters were analyzed, and 6867 differentially expressed (DE) mRNAs were screened. Functional analysis showed that DE mRNAs were associated with cell cycle and DNA replication. Protein–protein interaction network analysis revealed 20 hub genes, among which CENPF, BUB1, BUB1B, KIF23 and TTK had significant influence on prognosis. In addition, 807 DE lncRNAs and 243 DE miRNAs were identified. CeRNA network analysis indicated that AC079160.1-miR-539-5p-CENPF may be an important regulatory axis that potentially regulates the progression of LUAD. The expression of AC079160.1 and CENPF were positively correlated with hypoxia and stemness index, while miR-539-5p expression level was negatively correlated with hypoxia and stemness index. Overall, we identified CENPF, BUB1, BUB1B, KIF23 and TTK as potentially key genes involved in regulating hypoxia-induced tumor cell stemness, and found that AC079160.1-miR-539-5p-CENPF axis may be involved in regulating hypoxia induced tumor cell stemness in LUAD.

Carcinosarcoma (CS) of the uterus or ovary is a rare, aggressive and biphasic neoplasm composed of carcinoma and sarcoma elements. Previous genomic studies have identified the driver genes and genomic properties associated with CS. However, there is still no molecular subtyping scheme with clinical relevance for this disease. Here, we sequence 109 CS samples, focusing on 596 genes. We identify four molecular subtypes that resemble those observed in endometrial carcinoma: POLE -mutated, microsatellite instability, copy number high, and copy number low subtypes. These molecular subtypes are linked with DNA repair deficiencies, potential therapeutic strategies, and multiple clinicopathological features, including patient outcomes. Multi-regional comparative sequencing reveals genomic alteration-independent CS cell differentiation. Transcriptome and DNA methylome analyses confirm epithelial-mesenchymal transition as a mechanism of sarcoma differentiation. The current study thus provides therapeutic possibilities for CS as well as clues to understanding the molecular histogenic mechanism of its development. Carcinosarcoma of the ovary or uterus comprises both carcinoma and sarcoma elements. Here, the authors perform a multi -omics study of the disease revealing therapeutic possibilities for this rare and aggressive disease.

By integrating the expression profiles of long noncoding RNAs (lncRNAs) with clinical outcome and somatic copy-number alteration, the authors identified new lncRNAs that are associated with certain cancer subtypes and clinical prognoses. Experimental validation of the prostate cancer cell growth dependence of two new lncRNAs demonstrates the power of this approach for discovering disease-related lncRNAs. Despite growing appreciation of the importance of long noncoding RNAs (lncRNAs) in normal physiology and disease, our knowledge of cancer-related lncRNAs remains limited. By repurposing microarray probes, we constructed expression profiles of 10,207 lncRNA genes in approximately 1,300 tumors over four different cancer types. Through integrative analysis of the lncRNA expression profiles with clinical outcome and somatic copy-number alterations, we identified lncRNAs that are associated with cancer subtypes and clinical prognosis and predicted those that are potential drivers of cancer progression. We validated our predictions by experimentally confirming prostate cancer cell growth dependence on two newly identified lncRNAs. Our analysis provides a resource of clinically relevant lncRNAs for the development of lncRNA biomarkers and the identification of lncRNA therapeutic targets. It also demonstrates the power of integrating publically available genomic data sets and clinical information for discovering disease-associated lncRNAs.

The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. German Cancer Aid, Else Kröner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program.

Epstein-Barr virus (EBV) is associated with roughly 10% of gastric carcinomas worldwide (EBVaGC). Although previous investigations provide a strong link between EBV and gastric carcinomas, these studies were performed using selected EBV gene probes. Using a cohort of gastric carcinoma RNA-seq data sets from The Cancer Genome Atlas (TCGA), we performed a quantitative and global assessment of EBV gene expression in gastric carcinomas and assessed EBV associated cellular pathway alterations. EBV transcripts were detected in 17% of samples but these samples varied significantly in EBV coverage depth. In four samples with the highest EBV coverage (hiEBVaGC - high EBV associated gastric carcinoma), transcripts from the BamHI A region comprised the majority of EBV reads. Expression of LMP2, and to a lesser extent, LMP1 were also observed as was evidence of abortive lytic replication. Analysis of cellular gene expression indicated significant immune cell infiltration and a predominant IFNG response in samples expressing high levels of EBV transcripts relative to samples expressing low or no EBV transcripts. Despite the apparent immune cell infiltration, high levels of the cytotoxic T-cell (CTL) and natural killer (NK) cell inhibitor, IDO1, was observed in the hiEBVaGCs samples suggesting an active tolerance inducing pathway in this subgroup. These results were confirmed in a separate cohort of 21 Vietnamese gastric carcinoma samples using qRT-PCR and on tissue samples using in situ hybridization and immunohistochemistry. Lastly, a panel of tumor suppressors and candidate oncogenes were expressed at lower levels in hiEBVaGC versus EBV-low and EBV-negative gastric cancers suggesting the direct regulation of tumor pathways by EBV.

We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes ( TP53 , PIK3CA and GATA3 ) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3 , PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer. The Cancer Genome Atlas Network describe their multifaceted analyses of primary breast cancers, shedding light on breast cancer heterogeneity; although only three genes ( TP53 , PIK3CA and GATA3 ) are mutated at a frequency greater than 10% across all breast cancers, numerous subtype-associated and novel mutations were identified. Gene variation in breast cancer This Article from the Cancer Genome Atlas consortium describes a multifaceted analysis of primary breast cancers in 825 people. Exome sequencing, copy number variation, DNA methylation, messenger RNA arrays, microRNA sequencing and proteomic analyses were performed and integrated to shed light on breast-cancer heterogeneity. Just three genes — TP53 , PIK3CA and GATA3 — are mutated at greater than 10% frequency across all breast cancers. Many subtype-associated and novel mutations were identified, as well as two breast-cancer subgroups with specific signalling-pathway signatures. The analyses also suggest that much of the clinically observable plasticity and heterogeneity occurs within, and not across, the major subtypes of breast cancer.

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets. In cancer, long noncoding RNAs (lncRNAs) can regulate immune-related pathways. Here, the authors present ImmLnc, an algorithm that can help prioritise immune-related lncRNAs in cancer immunotherapy research