Long non-coding RNA TDRG1 facilitates cell proliferation, migration and invasion in breast cancer via targeting miR-214-5p/CLIC4 axis

Ginsenoside Rb1 Inhibits the Proliferation of Lung Cancer Cells by Inducing the Mitochondrial-mediated Apoptosis Pathway

BACKGROUND

Lung cancer is one of the more common malignant tumors posing a great threat to human life, and it is very urgent to find safe and effective therapeutic drugs. The antitumor effect of ginsenosides has been reported to be a treatment with a strong effect and a high safety profile.

OBJECTIVE

This paper aimed to investigate the inhibitory effect of ginsenoside Rb1 on 95D and NCI-H460 lung cancer cells and its pathway to promote apoptosis.

METHODS

We performed the CCK-8 assay, fluorescence staining assay, flow cytometry, scratch healing assay, and Transwell assay to detect the effects of different concentrations of ginsenoside Rb1 on the antitumor activity of 95D and NCI-H460 cells and Western Blot detected the mechanism of antitumor effect.

RESULTS

Ginsenoside Rb1 treatment significantly increased the inhibition and apoptosis rates of 95D and NCIH460 cells and inhibited the cell cycle transition from S phase to G2/M. Rb1 induces apoptosis by altering the levels of P53, Bax, Cyto-c, Caspase-8, Caspase-3, Cleaved Caspase-3, Bcl-2, MMP-2, and MMP-9 proteins and activating the external apoptotic pathway.

CONCLUSION

Ginsenoside Rb1 inhibits proliferation and migration and induces apoptosis of 95D and NCI-H460 lung cancer cells by regulating the mitochondrial apoptotic pathway to achieve antitumor activity.

Multi-omics integration method based on attention deep learning network for biomedical data classification

BACKGROUND AND OBJECTIVE

Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics.

METHODS

We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction.

RESULTS

We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets.

CONCLUSIONS

MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data.

Measuring functional similarity of lncRNAs based on variable K-mer profiles of nucleotide sequences

Long non-coding RNAs are a class of essential non-coding RNAs with a length of more than 200 nts. Recent studies have indicated that lncRNAs have various complex regulatory functions, which play great impacts on many fundamental biological processes. However, measuring the functional similarity between lncRNAs by traditional wet-experiments is time-consuming and labor intensive, computational-based approaches have been an effective choice to tackle this problem. Meanwhile, most sequences-based computation methods measure the functional similarity of lncRNAs with their fixed length vector representations, which could not capture the features on larger k-mers. Therefore, it is urgent to improve the predict performance of the potential regulatory functions of lncRNAs. In this study, we propose a novel approach called MFSLNC to comprehensively measure functional similarity of lncRNAs based on variable k-mer profiles of nucleotide sequences. MFSLNC employs the dictionary tree storage, which could comprehensively represent lncRNAs with long k-mers. The functional similarity between lncRNAs is evaluated by the Jaccard similarity. MFSLNC verified the similarity between two lncRNAs with the same mechanism, detecting homologous sequence pairs between human and mouse. Besides, MFSLNC is also applied to lncRNA-disease associations, combined with the association prediction model WKNKN. Moreover, we also proved that our method can more effectively calculate the similarity of lncRNAs by comparing with the classical methods based on the lncRNA-mRNA association data. The detected AUC value of prediction is 0.867, which achieves good performance in the comparison of similar models.

LINC00536 Promotes Breast Cancer Progression by Regulating ROCK1 via Sponging of miR-214-5p

Accumulating evidence has shown that long noncoding RNAs (lncRNAs) play a significant role in regulating gene expression and participating in the progression of various malignancies. In our study, by analyzing data from The Cancer Genome Atlas (TCGA), LINC00536 was found to be highly expressed in breast cancer (BC) tissues, but its function and clinical significance in BC are still unknown. Therefore, we aimed to explore the role and molecular mechanism of LINC00536 in BC. We collected human BC tissue specimens and validated that LINC00536 was overexpressed in BC tissues. Increased LINC00536 expression was associated with advanced TNM stage, larger tumor diameter, lymph node metastasis and poor prognosis in patients with BC. Univariate and multivariate Cox regression analyses showed that high LINC00536 expression was an independent prognostic risk factor for overall survival in BC patients. Furthermore, quantitative reverse transcription PCR (qRT-PCR) showed that LINC00536 was upregulated in BC cell lines. Then, we confirmed that LINC00536 silencing-inhibited BC cell proliferation, migration, and invasion and led to cell cycle arrest in vitro. Animal experiments showed that knockdown of LINC00536 expression suppressed tumorigenesis in vivo. Mechanistically, LINC00536 serves as a ceRNA for miR-214-5p, increasing the expression of ROCK1, which acts as a tumor promoter in BC. Rescue assays revealed that miR-214-5p inhibition or ROCK1 overexpression could neutralize the suppressive effects of LINC00536 knockdown on cell proliferation, migration and invasion. Our data indicated that LINC00536 accelerates BC progression by regulating the miR-214-5p/ROCK1 pathway, which might provide a new perspective to investigate the development process of BC.

A Novel Identified Long Intergenic Noncoding RNA, LINC01574, Contributes to Breast Cancer Deterioration via the Regulation of miR-6745/TTYH3 Axis

Objective

Compelling evidence suggested that lncRNAs performed vital functions in the development of breast cancer (BC). The study intended to mine the functional roles of LINC01574 in BC and further excavated its underlying regulatory mechanism.

Methods

The expression and prognosis of LINC01574 in BC were detected by integrating analysis of data mining, bioinformatics, and RT-qPCR. Then, the effect of LINC01574 knockdown on BC cell growth and metastasis was evaluated in vitro and in vivo. Interactions between miR-6745 and LINC01574 or TTYH3 were revealed by both target prediction and dual luciferase reporter assay.

Results

Our data found that LINC01574 was markedly elevated in BC tissues and cells and was an independent prognostic risk factor for patients with BC. Further functional studies revealed that knockdown of LINC01574 remarkably inhibited the growth and metastasis of BC cells in vitro and in vivo. Mechanistically, LINC01574 competitively binds with miR-6745 to prevent the degradation of TTYH3, thereby promoting the development of BC.

Conclusion

Our results unmasked a novel LINC01574/miR-6745/TTYH3 regulatory axis in BC progression and suggested that LINC01574 might be a promising prognostic indicator and therapeutic target for patients with BC.

Identification and validation of a seven m6A-related lncRNAs signature predicting prognosis of ovarian cancer

Background

Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6—methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).

Methods

The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients.

Results

Two hundred seventy-five m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan–Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.

Conclusions

In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09591-4.

The lncRNA DANCR promotes development of atherosclerosis by regulating the miR-214-5p/COX20 signaling pathway

BACKGROUND

Although long non-coding RNA differentiation antagonizing non-protein coding RNA (DANCR) has been reported to be involved in atherosclerosis (AS) development, its specific mechanism remains unclear.

METHODS

DANCR expression levels in blood samples of AS patients and oxidized low-density lipoprotein (ox-LDL) treated vascular smooth muscle cells (VSMCs) and human umbilical vein endothelial cells (HUVECs) were detected by quantitative real-time polymerase chain reaction (qRT-PCR). The small interfering RNA targeting DANCR (si-DANCR) was used to silence DANCR expression. Cell viability was assessed by CCK-8 assay. Cell apoptosis was evaluated by flow cytometry. Levels of inflammatory cytokines, anti-oxidative enzyme superoxide dismutase (SOD) activity, and malonaldehyde (MDA) were detected by specific commercial kits. An animal AS model was established to confirm the role of DANCR/microR-214-5p/COX20 (the chaperone of cytochrome c oxidase subunit II COX2) in AS development.

RESULTS

DANCR was significantly increased in the blood samples of AS patients and ox-LDL treated VSMCs and HUVECs. DANCR downregulation obviously increased viability and reduced apoptosis of ox-LDL-treated VSMCs and HUVECs. Meanwhile, DANCR downregulation reduced the levels of inflammatory cytokines, including interleukin (IL)-6 (IL-6), IL-1beta (IL-1β), IL-6 and tumor necrosis factor (TNF)-alpha (TNF-α) and MDA while increasing the SOD level in ox-LDL-treated VSMCs and HUVECs. DANCR regulated COX20 expression by acting as a competing endogenous RNA (ceRNA) of miR-214-5p. Rescue experiments demonstrated that miR-214-5p downregulation obviously attenuated si-DANCR-induced protective effects on ox-LDL-caused endothelial injury.

CONCLUSIONS

Our results revealed that DANCR promoted AS progression by targeting the miR-214-5p/COX20 axis, suggesting that DANCR might be a potential therapeutic target for AS.

Cervical cancer progression is regulated by SOX transcription factors: Revealing signaling networks and therapeutic strategies

Cervical cancer is the fourth common gynecologic cancer and is considered as second leading cause of death among women. Various strategies are applied in treatment of cervical cancer including radiotherapy, chemotherapy and surgery. However, cervical cancer cells demonstrate aggressive behavior in advanced phases, requiring novel strategies in their elimination. On the other hand, SOX proteins are transcription factors capable of regulating different molecular pathways and their expression varies during embryogenesis, disease development and carcinogenesis. In the present review, our aim is to reveal role of SOX transcription factors in cervical cancer. SOX transcription factors play like a double-edged sword in cancer. For instance, SOX9 possesses both tumor-suppressor and tumor-promoting role in cervical cancer. Therefore, exact role of each SOX members in cervical cancer has been discussed to direct further experiments for revealing other functions. SOX proteins can regulate proliferation and metastasis of cervical cancer cells. Furthermore, response of cervical cancer cells to chemotherapy and radiotherapy is tightly regulated by SOX transcription factors. Different downstream targets of SOX proteins such as Wnt signaling, EMT and Hedgehog have been identified. Besides, upstream mediators such as microRNAs, lncRNAs and circRNAs can regulate SOX expression in cervical cancer. In addition to pre-clinical studies, role of SOX transcription factors as prognostic and diagnostic tools in cervical cancer has been shown.