circRNADisease v2.0: an updated resource for high-quality experimentally supported circRNA-disease associations.

circRNADisease v2.0 is an enhanced and reliable database that offers experimentally verified relationships between circular RNAs (circRNAs) and various diseases. It is accessible at http://cgga.org.cn/circRNADisease/ or http://cgga.org.cn:9091/circRNADisease/. The database currently includes 6998 circRNA-disease entries across multiple species, representing a remarkable 19.77-fold increase compared to the previous version. This expansion consists of a substantial rise in the number of circRNAs (from 330 to 4246), types of diseases (from 48 to 330) and covered species (from human only to 12 species). Furthermore, a new section has been introduced in the database, which collects information on circRNA-associated factors (genes, proteins and microRNAs), molecular mechanisms (molecular pathways), biological functions (proliferation, migration, invasion, etc.), tumor and/or cell line and/or patient-derived xenograft (PDX) details, and prognostic evidence in diseases. In addition, we identified 7 159 865 relationships between mutations and circRNAs among 30 TCGA cancer types. Due to notable enhancements and extensive data expansions, the circRNADisease 2.0 database has become an invaluable asset for both clinical practice and fundamental research. It enables researchers to develop a more comprehensive understanding of how circRNAs impact complex diseases.

A Multiethnic Germline-Somatic Association Database Deciphers Multilayered and Interconnected Genetic Mutations in Cancer.

Inherited germline and acquired somatic alterations can both promote human tumor development. Elucidating the cooperation between somatic and germline genetic alterations that drive tumorigenesis could help inform precision cancer prevention and treatment strategies. Here, leveraging genomic genotyping and sequencing data from 9,029 patients with cancer with European, East Asian, and African ancestry, we performed a pan-cancer analysis to evaluate the associations between germline SNPs and somatic alterations, including single-nucleotide variant and small insertion/deletion mutations, copy-number variation, tumor mutational burden, and mutational signatures. Genome-wide significant germline-somatic pairs were abundant, and most of the associations were observed in one cancer type and one ancestry group. A user-friendly interactive Multiethnic Germline-Somatic Association (MGSA) database (http://wanglab-hkust.cn:3838/MGSA/) was developed, which can be used to query, browse, and download the results of the association analyses. Moreover, the MGSA database offers additional survival analysis and functional annotation. Together, this work provides a resource for uncovering the clinical and biological roles of associations between germline variants and somatic alterations in human cancer. SIGNIFICANCE: Comprehensive analysis of connections between germline variants and somatic events in cancer offers a resource for investigating the functional significance of genetic mutations and exploring genetic factors contributing to racial disparities.

Identifying predictors of glioma evolution from longitudinal sequencing.

Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed divergent longitudinal trajectories in gliomas, but early molecular features steering posttreatment cancer evolution remain unclear. Here, we collected sequencing and clinical data of initial-recurrent tumor pairs from 544 adult diffuse gliomas and performed multivariate analysis to identify early molecular predictors of tumor evolution in three diffuse glioma subtypes. We found that CDKN2A deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated with acquiring hypermutation at recurrence in the IDH-wild-type glioma. In all glioma subtypes, MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution for LOngitudinal data version 2), a machine learning model integrating features at diagnosis to forecast hypermutation and progression after treatment. CELLO2 successfully stratified patients into subgroups with distinct prognoses and identified a high-risk patient group featured by MYC gain with worse post-progression survival, from the low-grade IDH-mutant-noncodel subtype. We then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance by promoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals early predictors of cancer evolution under therapy and provides a resource for precision oncology targeting cancer dynamics in diffuse gliomas.

Endothelial hyperactivation of mutant MAP3K3 induces cerebral cavernous malformation enhanced by PIK3CA GOF mutation.

Cerebral cavernous malformations (CCMs) refer to a common vascular abnormality that affects up to 0.5% of the population. A somatic gain-of-function mutation in MAP3K3 (p.I441M) was recently reported in sporadic CCMs, frequently accompanied by somatic activating PIK3CA mutations in diseased endothelium. However, the molecular mechanisms of these driver genes remain elusive. In this study, we performed whole-exome sequencing and droplet digital polymerase chain reaction to analyze CCM lesions and the matched blood from sporadic patients. 44 of 94 cases harbored mutations in KRIT1/CCM2 or MAP3K3, of which 75% were accompanied by PIK3CA mutations (P = 0.006). AAV-BR1-mediated brain endothelial-specific MAP3K3I441M overexpression induced CCM-like lesions throughout the brain and spinal cord in adolescent mice. Interestingly, over half of lesions disappeared at adulthood. Single-cell RNA sequencing found significant enrichment of the apoptosis pathway in a subset of brain endothelial cells in MAP3K3I441M mice compared to controls. We then demonstrated that MAP3K3I441M overexpression activated p38 signaling that is associated with the apoptosis of endothelial cells in vitro and in vivo. In contrast, the mice simultaneously overexpressing PIK3CA and MAP3K3 mutations had an increased number of CCM-like lesions and maintained these lesions for a longer time compared to those with only MAP3K3I441M. Further in vitro and in vivo experiments showed that activating PI3K signaling increased proliferation and alleviated apoptosis of endothelial cells. By using AAV-BR1, we found that MAP3K3I441M mutation can provoke CCM-like lesions in mice and the activation of PI3K signaling significantly enhances and maintains these lesions, providing a preclinical model for the further mechanistic and therapeutic study of CCMs.

Epidemiological characteristics and genetic alterations in adult diffuse glioma in East Asian populations.

Understanding the racial specificities of diseases-such as adult diffuse glioma, the most common primary malignant tumor of the central nervous system-is a critical step toward precision medicine. Here, we comprehensively review studies of gliomas in East Asian populations and other ancestry groups to clarify the racial differences in terms of epidemiology and genomic characteristics. Overall, we observed a lower glioma incidence in East Asians than in Whites; notably, patients with glioblastoma had significantly younger ages of onset and longer overall survival than the Whites. Multiple genome-wide association studies of various cohorts have revealed single nucleotide polymorphisms associated with overall and subtype-specific glioma susceptibility. Notably, only 3 risk loci-5p15.33, 11q23.3, and 20q13.33-were shared between patients with East Asian and White ancestry, whereas other loci predominated only in particular populations. For instance, risk loci 12p11.23, 15q15-21.1, and 19p13.12 were reported in East Asians, whereas risk loci 8q24.21, 1p31.3, and 1q32.1 were reported in studies in White patients. Although the somatic mutational profiles of gliomas between East Asians and non-East Asians were broadly consistent, a lower incidence of EGFR amplification in glioblastoma and a higher incidence of 1p19q-IDH-TERT triple-negative low-grade glioma were observed in East Asian cohorts. By summarizing large-scale disease surveillance, germline, and somatic genomic studies, this review reveals the unique characteristics of adult diffuse glioma among East Asians, to guide clinical management and policy design focused on patients with East Asian ancestry.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

A Comprehensive RNA Study to Identify circRNA and miRNA Biomarkers for Docetaxel Resistance in Breast Cancer.

To investigate the relationship between non-coding RNAs [especially circular RNAs (circRNAs)] and docetaxel resistance in breast cancer, and to find potential predictive biomarkers for taxane-containing therapies, we have performed transcriptome and microRNA (miRNA) sequencing for two established docetaxel-resistant breast cancer (DRBC) cell lines and their docetaxel-sensitive parental cell lines. Our analyses revealed differences between circRNA signatures in the docetaxel-resistant and -sensitive breast cancer cells, and discovered circRNAs generated by multidrug-resistance genes in taxane-resistant cancer cells. In DRBC cells, circABCB1 was identified and validated as a circRNA that is strongly up-regulated, whereas circEPHA3.1 and circEPHA3.2 are strongly down-regulated. Furthermore, we investigated the potential functions of these circRNAs by bioinformatics analysis, and miRNA analysis was performed to uncover potential interactions between circRNAs and miRNAs. Our data showed that circABCB1, circEPHA3.1 and circEPHA3.2 may sponge up eight significantly differentially expressed miRNAs that are associated with chemotherapy and contribute to docetaxel resistance via the PI3K-Akt and AGE-RAGE signaling pathways. We also integrated differential expression data of mRNA, long non-coding RNA, circRNA, and miRNA to gain a global profile of multi-level RNA changes in DRBC cells, and compared them with changes in DNA copy numbers in the same cell lines. We found that Chromosome 7 q21.12-q21.2 was a common region dominated by multi-level RNA overexpression and DNA amplification, indicating that overexpression of the RNA molecules transcribed from this region may result from DNA amplification during stepwise exposure to docetaxel. These findings may help to further our understanding of the mechanisms underlying docetaxel resistance in breast cancer.

Characterization of a pathogenic variant in GBA for Parkinson's disease with mild cognitive impairment patients.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and mild cognitive impairment (MCI) is a well-established risk factor for the development of dementia in PD. A growing body of evidence suggests that low expression of glucocerebrosidase (GBA) promotes the transmission of α-synuclein (α-Syn) interpolymers and the progression of PD. However, how GBA mutations affect the pathogenesis of PD via abnormal aggregation of α-Syn is unclear, and no clinically valid PD-MCI genetic markers have been identified. Here, we first located a GBA eQTL, rs12411216, by analysing DHS, eQTL SNP, and transcription factor binding site data using the UCSC database. Subsequently, we found that rs12411216 was significantly associated with PD-MCI (P < 0.05) in 306 PD patients by genotyping. In exploring the relationship between rs12411216 and GBA expression, the SNP was found to be associated with GBA expression in 50 PD patients through qPCR verification. In a further CRISPR/Cas9-mediated genome editing module, the SNP was identified to cause a decrease in GBA expression, weaken enzymatic activity and enhance the abnormal aggregation of α-Syn in SH-SY5Y cells. Additionally, using an electrophoretic mobility shift assay, we confirmed that the binding efficiency of transcription factor E2F4 was affected by the rs12411216 SNP. In conclusion, our results showed that rs12411216 regulated GBA expression, supporting its potential role as a PD-MCI genetic biomarker and highlighting novel mechanisms underlying Parkinson's disease.

Meta-analysis of 16S rRNA Microbial Data Identified Distinctive and Predictive Microbiota Dysbiosis in Colorectal Carcinoma Adjacent Tissue.

As research focusing on the colorectal cancer fecal microbiome using shotgun sequencing continues, increasing evidence has supported correlations between colorectal carcinomas (CRCs) and fecal microbiome dysbiosis. However, large-scale on-site and off-site (surrounding adjacent) tissue microbiome characterization of CRC was underrepresented. Here, considering each taxon as a feature, we demonstrate a machine learning-based method to investigate tissue microbial differences among CRC, colorectal adenoma (CRA), and healthy control groups using 16S rRNA data sets retrieved from 15 studies. A total of 2,099 samples were included and analyzed in case-control comparisons. Multiple methods, including differential abundance analysis, random forest classification, cooccurrence network analysis, and Dirichlet multinomial mixture analysis, were conducted to investigate the microbial signatures. We showed that the dysbiosis of the off-site tissue of colonic cancer was distinctive and predictive. The AUCs (areas under the curve) were 80.7%, 96.0%, and 95.8% for CRC versus healthy control random forest models using stool, tissue, and adjacent tissue samples and 69.9%, 91.5%, and 89.5% for the corresponding CRA models, respectively. We also found that the microbiota ecologies of the surrounding adjacent tissues of CRC and CRA were similar to their on-site counterparts according to network analysis. Furthermore, based on the enterotyping of tissue samples, the cohort-specific microbial signature might be the crux in addressing classification generalization problems. Despite cohort heterogeneity, the dysbiosis of lesion-adjacent tissues might provide us with further perspectives in demonstrating the role of the microbiota in colorectal cancer tumorigenesis.IMPORTANCE Turbulent fecal and tissue microbiome dysbiosis of colorectal carcinoma and adenoma has been identified, and some taxa have been proven to be carcinogenic. However, the microbiomes of surrounding adjacent tissues of colonic cancerous tissues were seldom investigated uniformly on a large scale. Here, we characterize the microbiome signatures and dysbiosis of various colonic cancer sample groups. We found a high correlation between colorectal carcinoma adjacent tissue microbiomes and their on-site counterparts. We also discovered that the microbiome dysbiosis in adjacent tissues could discriminate colorectal carcinomas from healthy controls effectively. These results extend our knowledge on the microbial profile of colorectal cancer tissues and highlight microbiota dysbiosis in the surrounding tissues. They also suggest that microbial feature variations of cancerous lesion-adjacent tissues might help to reveal the microbial etiology of colonic cancer and could ultimately be applied for diagnostic and screening purposes.