PVT1 promotes resistance to 5‑FU in colon cancer via the miR‑486‑5p/CDK4 axis

Competitive endogenous RNA networks: Decoding the role of long non-coding RNAs and circular RNAs in colorectal cancer chemoresistance

Colorectal cancer (CRC) is recognized as one of the most common gastrointestinal malignancies across the globe. Despite significant progress in designing novel treatments for CRC, there is a pressing need for more effective therapeutic approaches. Unfortunately, many patients undergoing chemotherapy develop drug resistance, posing a significant challenge for cancer treatment. Non-coding RNAs (ncRNAs) have been found to play crucial roles in CRC development and its response to chemotherapy. However, there are still gaps in our understanding of interactions among various ncRNAs, such as long non-coding RNAs (lncRNAs), circular RNAs (circRNAs) and microRNAs (miRNAs). These ncRNAs can act as either oncogenes or tumour suppressors, affecting numerous biological functions in different cancers including CRC. A class of ncRNA molecules known as competitive endogenous RNAs (ceRNAs) has emerged as a key player in various cellular processes. These molecules form networks through lncRNA/miRNA/mRNA and circRNA/miRNA/mRNA interactions. In CRC, dysregulation of ceRNA networks has been observed across various cellular processes, including proliferation, apoptosis and angiogenesis. These dysregulations are believed to play a significant role in the progression of CRC and, in certain instances, may contribute to the development of chemoresistance. Enriching our knowledge of these dysregulations holds promise for advancing the field of diagnostic and therapeutic modalities for CRC. In this review, we discuss lncRNA- and circRNA-associated ceRNA networks implicated in the emergence and advancement of drug resistance in colorectal carcinogenesis.

Long non-coding RNA (lncRNA) PVT1 in drug resistance of cancers: Focus on pathological mechanisms

According to estimates, cancer will be the leading cause of death globally in 2022, accounting for 9.6 million deaths. At present, the three main therapeutic modalities utilized to treat cancer are radiation therapy, chemotherapy, and surgery. However, during treatment, tumor cells resistant to chemotherapy may arise. Drug resistance remains a major oppose since it often leads to therapeutic failure. Furthermore, the term "acquired drug resistance" describes the situation where tumor cells already display drug resistance before undergoing chemotherapy. However, little is still known about the basic mechanisms underlying chemotherapy-induced drug resistance. The development of new technologies and bioinformatics has led to the discovery of additional genes associated with drug resistance. Long noncoding RNA plasmacytoma variant translocation 1 (PVT1) has been linked to an increased risk of cancer, according to a growing body of research. Apart from biological functions associated with cell division, development, pluripotency, and cell cycle, lncRNA PVT1 contributes significantly to the regulation of various aspects of genome function, such as transcription, splicing, and epigenetics. The article will address the mechanism by which lncRNA PVT1 influences drug resistance in cancer cells.

GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations

BACKGROUND

A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs.

METHOD

In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations.

RESULTS

The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer.

CONCLUSIONS

The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

LncRNA-PVT1 was identified as a key regulator for TMZ resistance and STAT-related pathway in glioma

BACKGROUND

PVT1, a previously uncharacterized lncRNA, was identified as a critical regulator involved in multiple functions in tumor, including cell proliferation, cell motility, angiogenesis and so on. However, the clinical significance and underlying mechanism of PVT1 was not be fully explored in glioma.

METHODS

In this study, 1210 glioma samples with transcriptome data from three independent databases (CGGA RNA-seq, TCGA RNA-seq and GSE16011 cohorts) were enrolled in this study. Clinical information and genomic profiles containing somatic mutations and DNA copy numbers were collected from TCGA cohort. The R software was performed for statistical calculations and graphics. Furthermore, we validated the function of PVT1 in vitro.

RESULTS

The results indicated that higher PVT1 expression was associated with aggressive progression of glioma. Cases with higher PVT1 expression always accompanied by PTEN and EGFR alteration. In addition, functional analyses and western blot results suggested that PVT1 inhibited the sensitivity of TMZ chemotherapy via JAK/STAT signaling. Meanwhile, knockdown of PVT1 increased the sensitivity of TZM chemotherapy in vitro. Finally, high PVT1 expression was associated with reduced survival time and may serve as a strong prognostic indicator for gliomas.

CONCLUSIONS

This study demonstrated that PVT1 expression strongly correlated with tumor progression and chemo-resistance. PVT1 may become a potential biomarker for the diagnosis and treatment in glioma.

Sanguinarine promotes apoptosis of hepatocellular carcinoma cells via regulating the miR-497-5p/CDK4 axis

OBJECTIVE

To determine the effect of sanguinarine on the biological behavior of hepatocellular carcinoma (HCC) cells via regulating the miR-497-5p/cyclin-dependent kinase 4 (CDK4) axis.

METHODS

Swiss Target Prediction was used for target prediction of sanguinarine. The targets were analyzed with KEGG enrichment analysis, and CDK4 was included in this study. Target prediction website, Diana tools enrichment analysis, and dual-luciferase reporter assay were adopted to identify the target miRNAs for CDK4. We measured expression levels of CDK4 and miR-497-5p in cancerous tissues, normal liver L02 cells, HepG2 HCC cells and sanguinarine-treated HepG2 cells. The expression of CDK4/miR-497-5p in HCC cells was intervened by treating HCC cells with sanguinarine. Cell proliferation, invasion and apoptosis were measured with CCK8, Transwell and flow cytometry, respectively.

RESULTS

CDK4 was shown to be a target for sanguinarine. Compared with L02 cells, CDK4 expression in HCC cells was significantly increased, but sanguinarine inhibited the CDK4 expression in HCC cells. The proliferation and invasion of HCC cells were inhibited, and the apoptosis was promoted by sanguinarine, but these effects were reversed by CDK4 overexpression (both P<0.05). miR-497-5p was confirmed to be a target miRNA for CDK4, and its expression was decreased in HCC cells but could be promoted by sanguinarine. The effect of miR-497-5p knockdown on HCC cells was partially reversed by si-CDK4.

CONCLUSION

Sanguinarine inhibits the proliferation and invasion of HCC cells, and induces the apoptosis of HCC cells by regulating the expression of miR-497-5p/CDK4.