lncRNA DLEU2 Accelerates Oral Cancer Progression via miR-30a-5p/RAP1B Axis to Regulate p38 MAPK Signaling Pathway

Epigenetic regulation of HOXA3 and its impact on oral squamous cell carcinoma progression

OBJECTIVE

The role of homeobox A3 (HOXA3) in cancer progression is gaining prominence, however, to date, no studies have investigated its regulatory function in oral cancer. In this study, we explored the role of HOXA3 through epigenetic mechanisms.

METHODS

Clinical samples were collected from 25 potentially malignant oral lesions and 50 oral squamous cell carcinoma (OSCC) patients, categorized into low-stage and high-stage tumors. The promoter activity of HOXA3 was determined through cloning and luciferase assays. CpG methylation patterns across the gene were identified using methyl-capture sequencing. Gene expression was analyzed using RT‒qPCR. The Survminer R package was used to assess the clinical significance of 3' UTR methylation associated with overall survival. RNA‒RNA interactions were analyzed using RNAInter and TargetScan v8.0.

RESULTS

HOXA3 expression was upregulated in dysplasia and downregulated in advanced cancer stages, showing an inverse correlation with promoter methylation, suggesting epigenetic regulation by DNA methylation. Hypermethylation of the 3' UTR was associated with poor overall survival in advanced stages. Long noncoding RNAs and microRNAs may post-transcriptionally modulate HOXA3 in oral carcinogenesis.

CONCLUSION

CpG-specific hypermethylation in the 3' UTR may serve as a potential biomarker in OSCC.

From diagnosis to therapy: The critical role of lncRNAs in hepatoblastoma

According to findings, long non-coding RNAs (lncRNAs) serves an integral part in growth and development of a variety of human malignancies, including Hepatoblastoma (HB). HB is a rare kind of carcinoma of the liver that mostly affects kids and babies under the age of three. Its manifestations include digestive swelling, abdominal discomfort, and losing weight. This thorough investigation digs into the many roles that lncRNAs serve in HB, giving views into their varied activities as well as possible therapeutic consequences. The function of lncRNAs in HB cell proliferation, apoptosis, migratory and penetrating capacities, epithelial-mesenchymal transition, and therapy tolerance is discussed. Various lncRNA regulatory roles are investigated in depth, yielding information on their effect on essential cell processes such as angiogenesis, apoptosis, immunity, and growth. Circulating lncRNAs are currently acknowledged as potential indications for the initial stages of identification of cancer, with the ability to diagnose as well as forecast. In addition to their diagnostic utility, lncRNAs provide curative opportunities as locations and actors, contributing to the expanding landscape of cancer research. Several HB-linked lncRNAs have been demonstrated to exhibit abnormal expression and are involved in tumor-like characteristics via DNA, RNA, or protein binding or encoding short peptides. As a result, a better knowledge of lncRNA instability might bring fresh perspectives into HB etiology as well as innovative strategies for HB early diagnosis and therapy. We describe the abnormalities of lncRNA expression in HB and their tumor-suppressive or carcinogenic activities during HB carcinogenesis in this study. Furthermore, we explore lncRNAs' diagnostic and therapeutic possibilities in HB.

Circular RNA circMAN1A2 promotes ovarian cancer progression through the microRNA-135a-3p/IL1RAP/TAK1 pathway

Background

Ovarian cancer (OC) is the most lethal malignancy in women owing to its diagnosis only at the advanced stage. Elucidation of its molecular pathogenesis may help identify new tumor markers and targets for therapy. Circular RNAs (circRNAs) are stable, conserved, and functional biomolecules that can be used as effective biomarkers for various cancers.

Methods

In this study, a potential circRNA related to early diagnosis of OC, circMAN1A2, was analyzed. Overexpression/knockdown of circMAN1A2 in OC cells was used to decipher its effects on cell proliferation with a Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine (EdU), cell cycle, clone formation, and wound healing assay. RNA pull-down and Dual luciferase assay were used to explain the underlying mechanism by which circMAN1A2 regulates OC cell proliferation. In vivo, the effect of circMAN1A2 in OC was evaluated using nude mouse xenograft experiments.

Results

CircMAN1A2 was highly expressed in OC and promoted proliferation, clone formation, and tumorigenicity of OC cells. In addition, we found that circMAN1A2 acted as a sponge for microRNA (miR)-135a-3p; miR-135a-3p directly targeted the 3' untranslated region of interleukin 1 receptor accessory protein (IL1RAP) in OC cells, thereby regulating the phosphorylation of transforming growth factor-beta activated kinase 1 (TAK1), which resulted in promotion of OC cell growth.

Conclusions

CircMAN1A2 promotes OC cell proliferation by inhibiting the miR-135a-3p/IL1RAP/TAK1 axis. In conclusion, circMAN1A2 may be a biomarker for early detection of OC and a target for subsequent therapy.

GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations

Using computational models to predict potential lncRNA-disease associations (LDAs) has emerged as an effective supplement to bioexperiments for exploring the pathogenesis of diseases. However, current computational models still face limitations in their ability to learn the complex features of bionetworks. In this study, HGCNLDA, a model which combines graph convolutional network (GCN)-based aggregation, heterogeneous information fusion, and a bilinear-decoder to infer LDAs was proposed. Recognizing the need to extract essential features during data processing, our HGCNLDA explored four key steps for uncovering interaction patterns within the bionetwork: (1) a novel type of tripartite heterogeneous network, known as the lncRNA-disease-miRNA network (LDMN), was constructed using computed similarities and known associations. (2) Homogeneous and heterogeneous features of nodes were extracted from domains within the LDMN by a GCN-based encoder. (3) Feature fusions, including bipolymerization operations and attention mechanism, were employed to capture a more accurate and comprehensive representation of nodes. (4) Bilinear-decoder was used to rebuild the edge type (or rating type) for a specific node pair, resulting in the predicted association score. Through a 5-fold cross-validation on two data sets, namely, data set1 and data set2, our HGCNLDA consistently demonstrated superior performance compared to five related models. It almost achieved the highest AUROC and AUPR values on both data sets, especially on data set2 where the results obtained were more challenging and objective. Case studies involving three real cancer scenarios further validated the practicality of HGCNLDA in identifying potential LDAs in real-world contexts. The source code and data for this study are available at https://github.com/zywait/HGCNLDA.