LINC00649 promotes bladder cancer malignant progression by regulating the miR‑15a‑5p/HMGA1 axis

The Diagnostic and Therapeutic Role of snoRNA and lincRNA in Bladder Cancer

Bladder cancer is one of the most common malignancies of the urinary tract and can be divided into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Although the means of diagnosis and treatment have continually improved in recent years, the recurrence rate of bladder cancer remains high, and patients with MIBC typically have an unfavourable prognosis and a low quality of life. Emerging evidence demonstrates that long noncoding RNAs play a crucial role in the carcinogenesis and progression of bladder cancer. Long intergenic noncoding RNAs (lincRNAs) are a subgroup of long noncoding RNAs (lncRNAs) that do not overlap protein-coding genes. The potential role of lincRNAs in the regulation of gene expression has been explored in depth in recent years. Small nucleolar RNAs (snoRNAs) are a class of noncoding RNAs (ncRNAs) that mainly exist in the nucleolus, are approximately 60-300 nucleotides in length, and are hosted inside the introns of genes. Small nucleolar RNA host genes (SNHGs) have been associated with the origin and development of bladder cancer. In this review, we aim to comprehensively summarize the biological functions of these molecules in bladder cancer.

Prognostic significance and identification of basement membrane-associated lncRNA in bladder cancer

Based on the importance of basement membrane (BM) in cancer invasion and metastasis, we constructed a BM-associated lncRNA risk model to group bladder cancer (BCa) patients. Transcriptional and clinical data of BCa patients were downloaded from The Cancer Genome Atlas (TCGA), and the expressed genes of BM-related proteins were obtained from the BM-BASE database. We download the GSE133624 chip data from the GEO database as an external validation dataset. We screened for statistically different BM genes between tumors and adjacent normal tissues. Co-expression analysis of lncRNAs and differentially expressed BM genes was performed to identify BM-related lncRNAs. Then, differentially expressed BM-related lncRNAs (DEBMlncRNAs) between tumor and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select lncRNAs for risk assessment. LASSO analysis was performed to build a prognostic model. We constructed a model containing 8 DEBMlncRNAs (AC004034.1, AL662797.1, NR2F1-AS1, SETBP1-DT, AC011503.2, AC093010.2, LINC00649 and LINC02321). The prognostic risk model accurately predicted the prognosis of BCa patients and revealed that tumor aggressiveness and distant metastasis were associated with higher risk scores. In this model, we constructed a nomogram to assist clinical decision-making based on clinicopathological characteristics such as age, T, and N. The model also showed good predictive power for the tumor microenvironment and mutational burden. We validated the expression of eight lncRNAs using the dataset GSE133624 and two human bladder cancer cell lines (5637, BIU-87) and examined the expression and cellular localization of LINC00649 and AC011503.2 using a human bladder cancer tissue chip. We found that knockdown of LINC00649 expression in 5637 cells promoted the proliferation of 5637 cells.Our eight DEBMlncRNA risk models provide new insights into predicting prognosis, tumor invasion, and metastasis in BCa patients.

High Mobility Group A1 (HMGA1): Structure, Biological Function, and Therapeutic Potential

High mobility group A1 (HMGA1) is a nonhistone chromatin structural protein characterized by no transcriptional activity. It mainly plays a regulatory role by modifying the structure of DNA. A large number of studies have confirmed that HMGA1 regulates genes related to tumours in the reproductive system, digestive system, urinary system and haematopoietic system. HMGA1 is rare in adult cells and increases in highly proliferative cells such as embryos. After being stimulated by external factors, it will produce effects through the Wnt/β-catenin, PI3K/Akt, Hippo and MEK/ERK pathways. In addition, HMGA1 also affects the ageing, apoptosis, autophagy and chemotherapy resistance of cancer cells, which are linked to tumorigenesis. In this review, we summarize the mechanisms of HMGA1 in cancer progression and discuss the potential clinical application of targeted HMGA1 therapy, indicating that targeted HMGA1 is of great significance in the diagnosis and treatment of malignancy.

Deciphering the Non-Coding RNA Landscape of Pediatric Acute Myeloid Leukemia

Pediatric acute myeloid leukemia (pedAML) is a heterogeneous blood cancer that affects children. Although survival rates have significantly improved over the past few decades, 20-30% of children will succumb due to treatment-related toxicity or relapse. The molecular characterization of the leukemic stem cell, shown to be responsible for relapse, is needed to improve treatment options and survival. Recently, it has become clear that non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), play a role in the development of human diseases, including pediatric cancer. Nevertheless, non-coding RNA expression data in pedAML are scarce. Here, we explored lncRNA (n = 30,168) and miRNA (n = 627) expression in pedAML subpopulations (leukemic stem cells (LSCs) and leukemic blasts (L-blasts)) and their normal counterparts (hematopoietic stem cells and control myeloblasts). The potential regulatory activity of differentially expressed lncRNAs in LSCs (unique or shared with the L-blast comparison) on miRNAs was assessed. Moreover, pre-ranked gene set enrichment analyses of (anti-) correlated protein-coding genes were performed to predict the functional relevance of the differentially upregulated lncRNAs in LSCs (unique or shared with the L-blast comparison). In conclusion, this study provides a catalog of non-coding RNAs with a potential role in the pathogenesis of pedAML, paving the way for further translational research studies.

Integrated analysis of differentially expressed genes and a ceRNA network to identify hub lncRNAs and potential drugs for multiple sclerosis

OBJECTIVE

Multiple sclerosis (MS) is an autoimmune neuroinflammatory disease of the nervous system. However, the precise molecular mechanisms underlying MS have yet to be fully elucidated. In this study, our aim was to provide novel insight into the pathogenesis of MS and provide a resource for identifying new biomarkers and therapeutics for MS.

METHODS

In this study, we analyzed the gene expression profiles (GSE21942) and miRNA expression profiles (GSE61741) of MS patient samples that were downloaded from the GEO database and identified differentially expressed mRNAs and miRNAs (DEmRNAs, DEmiRNAs). Next, we constructed a protein-protein interaction (PPI) network and a MS-specific ceRNA network (MCEN) by integrating expression profiles, interaction pairs of mRNA-miRNAs and lncRNA-miRNAs. Then, according to the modular structure of the PPI network, we identified hub DEmRNAs and generated a ceRNA subnetwork so that we could analyze the key lncRNAs that were associated with MS.

RESULTS

We first identified 4 modules by constructing a PPI network using DEmRNAs. Functional enrichment analysis showed these modules were enriched in immune-related pathways. Then, we constructed the MCEN and the hub gene-associated ceRNA subnetwork using a comprehensive computational approach. We identified three key lncRNAs (LINC00649, TP73-AS1 and MALAT1) and further identified key lncRNA-mediated ceRNAs within the subnetwork. Finally, by analyzing LINC00649-miR-1275-CD20, we identified 6 drugs that may represent novel drugs for MS.

CONCLUSION

Collectively, our results provide novel insight for the discovery of biomarkers and therapeutics for MS and provide a suitable foundation from which to design future investigations of the pathogenic mechanisms associated with MS.