Potential mechanism of circRNA_000585 in cholangiocarcinoma

Transcription factor MAFK binds to circRPPH to regulate SIRT gene-mediated cellular pyroptosis and lung adenocarcinoma progression

OBJECTIVE

Lung adenocarcinoma (LUAD) represents the most prevalent subtype of lung cancer (LC), accounting for 50% of all LC cases, with its occurrence continuing to rise. Multiple pyroptotic pathway mediators are implicated in LC initiation. The study delved into the mechanism of circRPPH1 in pyroptosis in LUAD.

METHODS

We first examined the relationship between circRPPH1 and the clinical features of LUAD patients by analyzing circRNA gene expression profiles from the GEO database and validating findings with clinical samples. Downstream pathways influenced by circRPPH1 were identified and assessed for their roles in LUAD cell growth and pyroptosis using gain-and-loss-of-function assays.

RESULTS

circRPPH1 was significantly upregulated in LUAD and associated with poor prognosis. It binds to the transcription factor MAFK, enhancing SIRT1 expression and activating the MAFK-Keap1 signaling pathway. Reducing circRPPH1 expression decreased SIRT1 levels, inhibited cell proliferation both in vivo and ex vivo, and increased markers of cellular pyroptosis. Additionally, overexpressing SIRT1 mitigated the effects of circRPPH1 silencing.

CONCLUSION

circRPPH1 promotes LUAD progression by binding to MAFK, enhancing SIRT1 expression, and inhibiting cellular pyroptosis.

Role of angiomotin family members in human diseases (Review)

Angiomotin (Amot) family members, including Amot, Amot-like protein 1 (Amotl1) and Amot-like protein 2 (Amotl2), have been found to interact with angiostatins. In addition, Amot family members are involved in various physiological and pathological functions such as embryonic development, angiogenesis and tumorigenesis. Some studies have also demonstrated its regulation in signaling pathways such as the Hippo signaling pathway, AMPK signaling pathway and mTOR signaling pathways. Amot family members play an important role in neural stem cell differentiation, dendritic formation and synaptic maturation. In addition, an increasing number of studies have focused on their function in promoting and/or suppressing cancer, but the underlying mechanisms remain to be elucidated. The present review integrated relevant studies on upstream regulation and downstream signals of Amot family members, as well as the latest progress in physiological and pathological functions and clinical applications, hoping to offer important ideas for further research.

Circular RNA SMARCA5 inhibits cholangiocarcinoma via microRNA-95-3p/tumor necrosis factor receptor associated factor 3 axis

Enhancing research indicatedthat circular RNA (circRNA) acted a critical part in cholangiocarcinoma (CHOL) development. This research aims to discover the role of circRNA SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 5 (SMARCA5) in CHOL bio-progression, which has been proved to be downregulated in CHOL tissues. In this study, quantitative reverse transcription polymerase chain reaction was used to reveal the level and linkage of circRNA SMARCA5, miRNA-95-3p and TNF receptor-associated factor 3 gene (TRAF3) in CHOL tissues and cancer cells. The target sites of circRNA SMARCA5 and miRNA-95-3p were forecast by Starbase, and Targetscan was conducted to forecast the potential linkage points of TRAF3 and miRNA-95-3p, and which is affirmed by double luciferase reporter assay. CCK-8 and flow cytometry assay was carried to indicate cell viability. And apoptosis-related protein was counted by caspase3 activity and Western blot assay. CircRNA SMARCA5 was downregulated in CHOL cell lines and cancer samples. Besides, over-expression of SMARCA5 inhibited cell growth and promoted apoptotic rate. Dual-luciferase reporter assays presented that miRNA-95-3p could link with circRNA SMARCA5. Moreover, miRNA-95-3p was discovered highly expressed in CHOL. Interference of miRNA-95-3p repressed cell proliferation and raised the apoptosis. Importantly, TRAF3 was validated to be a downstream of miRNA-95-3p. Strengthen of miRNA-95-3p reversed the inhibitory impact of circRNA SMARCA5-plasmid transfection, and the results of miRNA-95-3p inhibitor were reversed by si-TRAF3. CircRNA SMARCA5 is involved in CHOL development by interosculating miRNA-95-3p/TRAF3 axis and may become a novel approach for treating CHOL.

Circular RNA in cholangiocarcinoma: A systematic review and bibliometric analysis

BACKGROUND

Cholangiocarcinoma (CCA) is a common primary liver malignancy with a poor prognosis. Many studies have demonstrated the involvement of circular RNAs (circRNAs) in tumorigenesis and progression.

METHODS

Four online databases (PubMed, Web of Science, Embase, and Scopus) were searched on May 04, 2023, for original papers regarding CCA and circRNAs. Bibliometric analysis of included studies was performed on R Studio and GraphPad Prism.

RESULTS

Thirty studies were included in the systematic review and bibliometric analysis. The systematic review showed that circRNAs were involved in CCA proliferation, invasion, metastasis, chemotherapy resistance, and other biological processes and were related to the prognosis of patients and many clinicopathological features. Exosomal circRNAs provide a new idea for the early diagnosis of CCA. The bibliometric analysis showed a significant upward trend in the number of studies on CCA and circRNAs. The 30 included papers had 201 authors and were published in 22 English journals. The first paper was published in 2018, and the second paper was the most cited (148 citations).

CONCLUSION

This systematic review and bibliometric analysis demonstrates that circRNAs in CCA have not been studied enough. CircRNAs play an important role in the occurrence and progression of CCA. They may become new targets for the diagnosis, treatment, and prognostic monitoring of CCA.

CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 - 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.

A review on the role of ncRNAs in the pathogenesis of cholangiocarcinoma

Cholangiocarcinoma is a rare tumor but a challenging cancer in terms of pathological changes, clinical manifestations and therapeutic options. Recent studies have provided evidence for participation of non-coding RNAs in the carcinogenic process of cholangiocarcinoma. We demonstrate the role of long non-coding RNAs, microRNAs and circular RNAs in the pathogenesis of cholangiocarcinoma and highlight their significant position as therapeutic targets and biomarkers for this type of cancer. We also list a number of molecular axes comprising these non-coding RNAs that represent potential targets for therapeutic options in cholangiocarcinoma, based on their significant roles in the regulation of cell proliferation, differentiation and apoptosis of these cells.

iGRLCDA: identifying circRNA-disease association based on graph representation learning

While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.

Cholangiopathies and the noncoding revolution

PURPOSE OF REVIEW

Noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) among others, have attracted a great deal of attention for their potential role as master regulators of gene expression and as therapeutic targets. This review focuses on recent advances on the role of ncRNAs in the pathogenesis, diagnosis and treatment of diseases of the cholangiocytes (i.e. cholangiopathies).

RECENT FINDINGS

In the recent years, there has been an exponential growth in the knowledge on ncRNAs and their role in cholangiopathies, particularly cholangiocarcinoma.

SUMMARY

Although several studies focused on miRNAs as noninvasive biomarkers for diagnosis and staging, several studies also highlighted their functions and provided new insights into disease mechanisms.

Response to comments on our article (Yin YL et al., Parasit Vectors, 10.1186/s13071-021-04739-w) by Yuqing Wang and colleagues

This letter responds to comments on our article (Yin YL et al., Parasit Vectors, 10.1186/s13071-021-04739-w) by Yuqing Wang and colleagues, who wrote a letter entitled "Microarray analysis of circular RNAs in HCT-8 cells infected with Cryptosporidium parvum" and discussed statistical procedures for microarray analysis during C. parvum infection. To further confirm our data, in this letter, a common R package for analyses of differentially expressed genes, namely DESeq2, with Benjamini-Hochberg correction, was used to analyze our microarray data and identified 26 significantly differentially expressed circRNAs using adjusted P value < 0.05 and | Log2 (fold change [FC]) | ≥ 1.0, including our circRNA ciRS-7 of interest. Therefore, the protocol for selecting circRNAs of interest for further study in our article is acceptable and did not affect the subsequent scientific findings in our article.