Identification of altered biological processes in heterogeneous RNA-sequencing data by discretization of expression profiles

FOXA1 regulates alternative splicing in prostate cancer

Dysregulation of alternative splicing in prostate cancer is linked to transcriptional programs activated by AR, ERG, FOXA1, and MYC. Here, we show that FOXA1 functions as the primary orchestrator of alternative splicing dysregulation across 500 primary and metastatic prostate cancer transcriptomes. We demonstrate that FOXA1 binds to the regulatory regions of splicing-related genes, including HNRNPK and SRSF1. By controlling trans-acting factor expression, FOXA1 exploits an "exon definition" mechanism calibrating alternative splicing toward dominant isoform production. This regulation especially impacts splicing factors themselves and leads to a reduction of nonsense-mediated decay (NMD)-targeted isoforms. Inclusion of the NMD-determinant FLNA exon 30 by FOXA1-controlled oncogene SRSF1 promotes cell growth in vitro and predicts disease recurrence. Overall, we report a role for FOXA1 in rewiring the alternative splicing landscape in prostate cancer through a cascade of events from chromatin access, to splicing factor regulation, and, finally, to alternative splicing of exons influencing patient survival.

Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification

Glioblastoma (GBM) is characterized by extensive genetic and phenotypic heterogeneity. However, it remains unexplored primarily how CpG island methylation abnormalities in promoter mediate glioblastoma typing. First, we presented a multi-omics scale map between glioblastoma sample clusters constructed based on promoter CpG island (PCGI) methylation-driven genes, using datasets including methylation profiles, expression profiles, and single-cell sequencing data from multiple highly annotated public clinical cohorts. Second, we identified differences in the tumor microenvironment between the two glioblastoma sample clusters and resolved key signaling pathways between cell clusters at the single-cell level based on comprehensive comparative analyses to investigate the reasons for survival differences between two of these clusters. Finally, we developed a diagnostic map and a prediction model for glioblastoma, and compared theoretical differences of drug sensitivity between two glioblastoma sample clusters. In summary, this study established a classification system for dissecting promoter CpG island methylation heterogeneity in glioblastoma and provides a new perspective for the diagnosis and treatment of glioblastoma.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

An Integrated Analysis of the Identified PRPF19 as an Onco-immunological Biomarker Encompassing the Tumor Microenvironment, Disease Progression, and Prognoses in Hepatocellular Carcinoma

Background: Targeting the mRNA splicing process has been identified as a therapeutic strategy for human cancer. PRPF19 is an RNA binding protein that is involved in pre-mRNA processing and repairing DNA damage; the aberrant expression of PRPF19 is potentially associated with carcinogenesis. However, the biological role of PRPF19 in hepatocellular carcinoma (HCC) is still elusive.

Methods: Data obtained from TCGA, Oncomine, and GEO were used to investigate the PRPF19 expression level and its role in tumor immune infiltration, prognosis, and the tumor progression of cohorts from HCC. Using various databases and tools (UALCAN, TIMER, TISMO, and PathCards), we presented the potential mechanisms of PFPF19 upregulation, PRPF19-related pathways, and its biological functions in liver cancer.

Results: For HCC, PRPF19 expression was found upregulated both in single tumor cells and tissues. Furthermore, the increased expression of PRPF19 was significantly correlated to clinical characteristics: advanced stage, vascular invasion, high AFP, and poor prognosis of HCC. According to the tumor-immunological analysis, we found that PRPF19 is positively correlated with infiltrating myeloid-derived suppressor cells (MDSCs). Moreover, the microenvironment of HCC tissues with high expression of PRPF19 is highly immunosuppressive (lower T-lymphocytes, multiple immune checkpoints upregulated). Patients with high expression of PRPF19 and high MDSCs had a worse survival prognosis as well. TP53 mutation may have a positive effect on PRPF19 expression via decreased promoter methylation of PRPF19. By TF-mRNA network analysis, key transcription factors (TFs) in TC-NER and PCS pathways (PRPF19 involved) were identified.

Conclusion: This work implied that PRPF19 is associated with tumor immune evasion and progression, and serves as a prognostic marker for worse clinical outcomes with HCC. Thus, this critical regulator could serve as a potential therapeutic target of HCC.

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

LncRNA BCYRN1 inhibits glioma tumorigenesis by competitively binding with miR-619-5p to regulate CUEDC2 expression and the PTEN/AKT/p21 pathway

Glioma is the most common malignant tumor in the central nervous system. Altered long noncoding RNAs (lncRNAs) are playing regulatory roles in physiological and pathogenic processes in cancer. Here, we uncovered a differentially expressed lncRNA called brain cytoplasmic RNA 1 (BCYRN1), and elucidated its function and molecular mechanism in the progression and development of glioma. Three fresh tumor tissues from glioma patients and three normal brain tissues from craniocerebral trauma patients were prepared for high-throughput RNA sequencing. Differential RNA transcripts and BCYRN1 were identified by RT-qPCR in glioma samples and controls. CCK-8, colony formation assays, flow cytometry, TUNEL assays, cell migration assays, wound-healing assays, and xenograft model were established to investigate the biological function of BCYRN1 both in vitro and in vivo. Various bioinformatics analysis, dual-luciferase reporter assays, biotinylated RNA pulldown assays, and rescue experiments were conducted to reveal the underlying mechanisms of competitive endogenous RNAs (ceRNAs). 183 lncRNAs were identified with significant dysregulation in glioma and randomly selected differential RNAs were further confirmed by RT-qPCR. Among them, BCYRN1 was the most downregulated lncRNA, and its low expression positively correlated with glioma progression. Functionally, BCYRN1 overexpression inhibited cell proliferation, migration in glioma cell lines, whereas BCYRN1 depletion resulted in the opposite way. MiR-619-5p was further confirmed as the direct target of BCYRN1. Mechanistically, miR-619-5p specifically targeted the CUE domain containing protein 2 (CUEDC2), and BCYRN1/miR-619-5p suppressed glioma tumorigenesis by inactivating PTEN/AKT/p21 pathway in a CUEDC2-dependent manner. Overall, our data presented that the reduced expression of BCYRN1 was associated with poor patient outcome in glioma. BCYRN1 functioned as a ceRNA to inhibit glioma progression by sponging miR-619-5p to regulate CUEDC2 expression and PTEN/AKT/p21 pathway. Our results indicated that BCYRN1 exerted tumor suppressor potential and might be a candidate in the diagnosis and treatment of glioma.