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As one of the most abundant RNA post-transcriptional modifications, N 6 -methyladenosine (m 6 A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m 6 A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m 6 A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m 6 A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m 6 A sites in S. cerevisiae . Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/ .

N6-methyladenosine (m 6 A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m 6 A sites within sequences since high-resolution mapping of m 6 A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m 6 A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m 6 A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/ . It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m 6 A site functions.

The role of focal amplifications and extrachromosomal DNA (ecDNA) is unknown in gastric cardia adenocarcinoma (GCA). Here, we identify frequent focal amplifications and ecDNAs in Chinese GCA patient samples, and find focal amplifications in the GCA cohort are associated with the chromothripsis process and may be induced by accumulated DNA damage due to local dietary habits. We observe diverse correlations between the presence of oncogene focal amplifications and prognosis, where ERBB2 focal amplifications positively correlate with prognosis and EGFR focal amplifications negatively correlate with prognosis. Large-scale ERBB2 immunohistochemistry results from 1668 GCA patients show survival probability of ERBB2 positive patients is lower than that of ERBB2 negative patients when their surviving time is under 2 years, however, the tendency is opposite when their surviving time is longer than 2 years. Our observations indicate that the ERBB2 focal amplifications may represent a good prognostic marker in GCA patients. The role of focal amplifications and extrachromosomal DNA (ecDNA) is unknown in gastric cardia adenocarcinoma (GCA). Here, the authors identify frequent focal amplifications and ecDNAs in Chinese GCA patient samples, as well as the potential associations of these alterations with prognosis and dietary habits.

There is ample support for developmental regulation of glioblastoma stem cells. To examine how cell lineage controls glioblastoma stem cell function, we present a cross-species epigenome analysis of mouse and human glioblastoma stem cells. We analyze and compare the chromatin-accessibility landscape of nine mouse glioblastoma stem cell cultures of three defined origins and 60 patient-derived glioblastoma stem cell cultures by assay for transposase-accessible chromatin using sequencing. This separates the mouse cultures according to cell of origin and identifies three human glioblastoma stem cell clusters that show overlapping characteristics with each of the mouse groups, and a distribution along an axis of proneural to mesenchymal phenotypes. The epigenetic-based human glioblastoma stem cell clusters display distinct functional properties and can separate patient survival. Cross-species analyses reveals conserved epigenetic regulation of mouse and human glioblastoma stem cells. We conclude that epigenetic control of glioblastoma stem cells primarily is dictated by developmental origin which impacts clinically relevant glioblastoma stem cell properties and patient survival. The epigenetic regulation of glioblastoma stem cell (GSC) function remains poorly understood. Here, the authors compare the chromatin accessibility landscape of GSC cultures from mice and patients and suggest that the epigenome of GSCs is cell lineage-regulated and could predict patient survival.

Formalin-fixed paraffin-embedded (FFPE) samples are the gold standard for tissue preservation in clinical and research settings. Current single-cell chromatin accessibility technologies cannot resolve cell-type-specific epigenetic profiles in FFPE tissues due to extensive DNA damage. We present scFFPE-ATAC, a high-throughput single-cell chromatin accessibility assay for FFPE samples that integrates an FFPE-adapted Tn5 transposase, ultra-high-throughput DNA barcoding (>56 million barcodes per run), T7 promoter-mediated DNA damage repair, and in vitro transcription. We benchmark scFFPE-ATAC on FFPE mouse spleen and validate its performance against fresh tissue. We apply it to human lymph node samples archived for 8–12 years and to lung cancer FFPE tissues, revealing distinct regulatory trajectories between tumor center and invasive edge. Analysis of archived follicular lymphoma and transformed diffuse large B-cell lymphoma samples identifies relapse- and transformation-associated epigenetic dynamics. scFFPE-ATAC enables retrospective, spatial, and mechanistic epigenetic studies in long-term archived specimens. The study introduces scFFPE-ATAC, a high-throughput single-cell chromatin accessibility profiling method enabling profiling from clinically archived formalin-fixed paraffin-embedded (FFPE) tissues, unlocking retrospective epigenetic insights across cancer and human diseases.

As one of the most abundant RNA post-transcriptional modifications, N -methyladenosine (m A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m A sites in S. cerevisiae. Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/.

Cutaneous squamous cell carcinoma (cSCC) is a fast-increasing cancer with metastatic potential. Extracellular vesicles (EVs) are small membrane-bound vesicles that play important roles in intercellular communication, particularly in the tumor microenvironment (TME). Here we report that cSCC cells secrete an increased number of EVs relative to normal human epidermal keratinocytes (NHEKs) and that interfering with the capacity of cSCC to secrete EVs inhibits tumor growth in vivo in a xenograft model of human cSCC. Transcriptome analysis of tumor xenografts by RNA-sequencing enabling the simultaneous quantification of both the human and the mouse transcripts revealed that impaired EV-production of cSCC cells prominently altered the phenotype of stromal cells, in particular genes related to extracellular matrix (ECM)-formation and epithelial-mesenchymal transition (EMT). In line with these results, co-culturing of human dermal fibroblasts (HDFs) with cSCC cells, but not with normal keratinocytes in vitro resulted in acquisition of cancer-associated fibroblast (CAF) phenotype. Interestingly, EVs derived from metastatic cSCC cells, but not primary cSCCs or NHEKs, were efficient in converting HDFs to CAFs. Multiplex bead-based flow cytometry assay and mass-spectrometry (MS)-based proteomic analyses revealed the heterogenous cargo of cSCC-derived EVs and that especially EVs derived from metastatic cSCCs carry proteins associated with EV-biogenesis, EMT, and cell migration. Mechanistically, EVs from metastatic cSCC cells result in the activation of TGFβ signaling in HDFs. Altogether, our study suggests that cSCC-derived EVs mediate cancer-stroma communication, in particular the conversion of fibroblasts to CAFs, which eventually contribute to cSCC progression.

Cancer is a genetic disease that arises from cells undergoing genomic alterations. Understanding the role of genomic instability in tumorigenesis, progression, and metastasis is crucial for advancing cancer diagnosis, treatment, and drug development. The development of cancer is driven by a combination of genetic variations, epigenetic dysregulation, and environmental influences. Among the most aggressive malignancies worldwide, gastroesophageal cancer and glioblastoma are characterized by extremely poor prognoses and limited therapeutic options. We explored GCA from a genomic perspective, integrating whole exome sequencing, RNAseq, proteomics, and metabolomics to identify key genetic alterations and signaling pathways that drive tumorigenesis. Additionally, we investigated the epigenetic landscape of glioblastoma to reveal the role of epigenetic dysregulation in tumor heterogeneity and progression.In Paper I, we performed whole genome sequencing on 36 pairs of tumor and tumor-matched normal samples from a GCA cohort and conducted immunohistochemistry of HER2 in 1668 GCA patients. We found that focal amplifications were detected in 77.8% of all cases, while ecDNAs were identified in 52.8% of total cases. Surprisingly, we found patients with ERBB2 focal amplification or IHC HER2 positive staining are associated with better prognosis, which is inconsistent with many of the previous studies that the oncogene ERBB2 typically correlates with poorer prognosis in patients.In Paper II, we conducted multi-omics profiling of 128 GCA patients, categorizing them into HER2-high, HER2-low, and HER2-negative groups. HER2 was identified as a favorable prognostic marker, with DNA repair features enriched in the HER2-high group and inflammation predominant in the HER2-low and HER2-negative groups. ARID1A mutations were particularly prognostic in the HER2-negative group. Our findings suggest antiinflammatory therapies and CD47/SIRPA immune checkpoint inhibition as potential strategies for HER2-negative GCA, offering new avenues for personalized treatment.In Paper III, we integrated analyses of chromatin accessibility, histone modifications (H3K4me1, H3K4me3, H3K27ac, H3K27me3), and chromatin loops in human and mouse GSCs. We found the enhancer marker H3K4me1 and the repressive marker H3K27me3 in human GSCs could separate patients into two groups with significant survival differences and enhancer signatures that define glioblastoma stem cell subtypes. Transcription factor (TF) enrichment analysis suggested that neural progenitor lineage-specific TFs, such as OLIG2, SOX4, POU family TFs are acting TFs in different types of enhancers and determine the lineage specificity of human GSCs. Cross-species analysis between human and mouse GSCs identified key TFs that define lineage-specific subtypes and observed both conserved and species-specific TFs.

Selecting informative genes, including individually discriminant genes and synergic genes, from expression data has been useful for medical diagnosis and prognosis. Detecting synergic genes is more difficult than selecting individually discriminant genes. Several efforts have recently been made to detect gene-gene synergies, such as dendrogram-based I ( X 1 ; X 2 ; Y) (mutual information), doublets (gene pairs) and MIC ( X 1 ; X 2 ; Y ) based on the maximal information coefficient. It is unclear whether dendrogram-based I ( X 1 ; X 2 ; Y ) and doublets can capture synergies efficiently. Although MIC( X 1 ; X 2 ; Y ) can capture a wide range of interaction, it has a high computational cost triggered by its 3-D search. In this paper, we developed a simple and fast approach based on abs conversion type ( i.e . Z = | X 1  −  X 2 |) and t -test, to detect interactions in simulation and real-world datasets. Our results showed that dendrogram-based I ( X 1 ; X 2 ; Y ) and doublets are helpless for discovering pair-wise gene interactions, our approach can discover typical pair-wise synergic genes efficiently. These synergic genes can reach comparable accuracy to the individually discriminant genes using the same number of genes. Classifier cannot learn well if synergic genes have not been converted properly. Combining individually discriminant and synergic genes can improve the prediction performance.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) constitutes a rare and aggressive malignancy originating from plasmacytoid dendritic cells (pDCs) with a primarily cutaneous tropism followed by dissemination to the bone marrow and other organs. We conducted a genome-wide analysis of the tumor methylome in an extended cohort of 45 BPDCN patients supplemented by WES and RNA-seq as well as ATAC-seq on selected cases. We determined the BPDCN DNA methylation profile and observed a dramatic loss of DNA methylation during malignant transformation from early and mature DCs towards BPDCN. DNA methylation profiles further differentiate between BPDCN, AML, CMML, and T-ALL exhibiting the most striking global demethylation, mitotic stress, and merely localized DNA hypermethylation in BPDCN resulting in pronounced inactivation of tumor suppressor genes by comparison. DNA methylation-based analysis of the tumor microenvironment by MethylCIBERSORT yielded two, prognostically relevant clusters (IC1 and IC2) with specific cellular composition and mutational spectra. Further, the transcriptional subgroups of BPDCN (C1 and C2) differ by DNA methylation signatures in interleukin/inflammatory signaling genes but also by higher transcription factor activity of JAK-STAT and NFkB signaling in C2 in contrast to an EZH2 dependence in C1-BPDCN. Our integrative characterization of BPDCN offers novel molecular insights and potential diagnostic applications.