Long non-coding RNA SNHG16 promotes cell proliferation and invasion in lung adenocarcinoma via sponging let-7a-5p

Guo, Yuxia; Zhen, Honghua; Chen, Wenping; Sun, Yue; Li, Kun; Feng, Eryan

doi:10.23736/s0026-4733.19.08140-9

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…The 5-year and 10-year cure rates were 43.9% and 29.0%, respectively [ 55 ]. In the prediction results of the proposed model, 14 of the first 20 lncRNAs associated with adenocarcinoma had been already proved by relevant medicine: lncRNA XIST promoted human lung adenocarcinoma cells to cisplatin resistance via let-7i/BAG-1 axis [ 56 ]; lncRNA MALAT1 promoted gastric adenocarcinoma through the miR-181a-5p/AKT3 axis [ 57 ]; lncRNA CTB-89H12.4 regulated phosphatase and tensin homolog expression in prostate cancer [ 58 ]; lncRNA HCG18 acted on the oncogene in lung adenocarcinoma and enhanced lung adenocarcinoma progression by targeting miR-34a-5p/HMMR axis [ 59 ]; and lncRNA SNHG16 promoted cell proliferation and invasion in lung adenocarcinoma via sponging let-7a-5p [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

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lncRNA-disease association prediction based on the weight matrix and projection score

Wang

Zhang

et al. 2023

PLoS ONE

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With the development of medical science, long noncoding RNA (lncRNA), originally considered as a noise gene, has been found to participate in a variety of biological activities. Several recent studies have shown the involvement of lncRNA in various human diseases, such as gastric cancer, prostate cancer, lung cancer, and so forth. However, obtaining lncRNA-disease relationship only through biological experiments not only costs manpower and material resources but also gains little. Therefore, developing effective computational models for predicting lncRNA-disease association relationship is extremely important. This study aimed to propose an lncRNA-disease association prediction model based on the weight matrix and projection score (LDAP-WMPS). The model used the relatively perfect lncRNA-miRNA relationship data and miRNA-disease relationship data to predict the lncRNA-disease relationship. The integrated lncRNA similarity matrix and the integrated disease similarity matrix were established by fusing various methods to calculate the similarity between lncRNA and disease. This study improved the existing weight algorithm, applied it to the lncRNA-miRNA-disease triple network, and thus proposed a new lncRNA-disease weight matrix calculation method. Combined with the improved projection algorithm, the lncRNA-miRNA relationship and miRNA-disease relationship were used to predict the lncRNA-disease relationship. The simulation results showed that under the Leave-One-Out-Cross-Validation framework, the area under the receiver operating characteristic curve of LDAP-WMPS could reach 0.8822, which was better than the latest result. Taking adenocarcinoma and colorectal cancer as examples, the LDAP-WMPS model was found to effectively infer the lncRNA-disease relationship. The simulation results showed good prediction performance of the LDAP-WMPS model, which was an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease relationship data.

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Section: Resultsmentioning

confidence: 99%

“…phosphatase and tensin homolog expression in prostate cancer [58]; lncRNA HCG18 acted on the oncogene lung adenocarcinoma and enhanced lung adenocarcinoma progression by targeting miR-34a-5p/HMMR axis [59]; and lncRNA SNHG16 promoted cell proliferation and invasion in lung adenocarcinoma via sponging let-7a-5p [60].…”

Section: Plos Onementioning

confidence: 99%

lncRNA-disease association prediction based on the weight matrix and projection score

Wang

Zhang

et al. 2023

PLoS ONE

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“…Lin Guoji reported 68 cases of adenocarcinoma. The 5-year and 10-year cure rates were 43.9% and 29.0% respectively [43].In our prediction results, 14 of the first 20 lncRNAs associated with Adenocarcinoma have been proved by relevant medicine:lncRNA XIST promotes human lung adenocarcinoma cells to cisplatin resistance via let-7i/BAG-1 axis [44].lncRNA MALAT1 promotes gastric adenocarcinoma through the miR-181a-5p/AKT3 axis [45].lncRNA CTB-89H12.4 regulation of PTEN expression in prostate cancer [46].lncRNA HCG18 acted an oncogene in lung adenocarcinoma and enhanced lung adenocarcinoma progression by targeting miR-34a-5p/HMMR axis [47].lncRNA SNHG16 promotes cell proliferation and invasion in lung adenocarcinoma via sponging let-7a-5p [48].…”

Section: Case Studiesmentioning

confidence: 99%

lncRNA-Disease Association Prediction Based On Weight Matrix And Projection Score

Wang

Zhang

et al. 2021

Preprint

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Background: with the development of medical science, lncRNA, originally considered as a noise gene, has been found to participate in a variety of biological activities. Nowadays, more and more studies show that lncRNA is involved in various human diseases, such as gastric cancer, prostate cancer, lung cancer, etc. However, obtaining lncRNA-disease association only through biological experiments not only costs manpower and material resources, but also gains little. Therefore, it is very important to develop effective computational models for predicting lncRNA-disease association. Results: In this paper, a new lncRNA-disease association prediction model LDAP-WMPS based on weight distribution and projection score is proposed. Based on the existing research results of disease semantic similarity, the integrated lncRNA similarity matrix and the integrated disease similarity matrix are calculated according to the disease semantic similarity and the association information between data. On this basis, the weight algorithm is combined with the improved projection algorithm to predict the lncRNA-disease association through the known lncRNA-miRNA association and miRNA-disease association. The simulation results show that under the loocv framework, the AUC of LDAP-WMPS can reach 0.8822. Better than the latest results. Through the case study of adenocarcinoma and colorectal cancer, it is proved that LDAP-WMPS can effectively infer lncRNA-disease association. Conclusions: The simulation results show that LDAP-WMPS has good prediction performance, which is an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease association data. Keywords: lncRNA-miRNA association, miRNA-disease association, disease semantic similarity, Integrated lncRNA similarity, integrated disease similarity, Weight allocation algorithm, Projection score.

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“…For instance, increased expression of SNHG16 has been shown to be associated with poor prognosis of lung cancer [15]. Guo et al reported that SNHG16 promoted the proliferation and invasion of lung adenocarcinoma cells via sponging of let-7a-5p [16]. Similarly, Yu et al showed the involvement of SNHG16 in the development of non-smallcell lung cancer development [17].…”

Section: Introductionmentioning

confidence: 99%

Long Noncoding RNA SNHG16 Regulates the Growth of Human Lung Cancer Cells by Modulating the Expression of Aldehyde Dehydrogenase 2 (ALDH2)

Jiang

ZhaoCheng

et al. 2022

Journal of Oncology

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The involvement of long noncoding RNA (lncRNA) SNHG16 has been reported in several human cancers. Notwithstanding, the role of lncRNA SNHG16 is yet largely unknown in human lung cancer. Consequently, this study was undertaken to investigate the role and therapeutic potential of SNHG16 in human lung cancer. The results showed a significant ( P < 0.05 ) transcriptional upregulation of SNHG16 in lung cancer tissues and cell lines. However, downregulation of SNHG16 resulted in significant ( P < 0.05 ) inhibition of lung cancer A549 and SK-LU-1 cell proliferation. DAPI and annexin V/PI assays revealed apoptosis to be responsible for inhibition of cell proliferation and colony formation observed upon SNHG16 knockdown. This was accompanied by enhancement of Bax and suppression of Bcl-2 expression in A549 and SK-LU-1 cells. Transwell assays revealed that silencing of SNHG16 also significantly ( P < 0.05 ) inhibited migration and invasion of A549 and SK-LU-1 cells. Bioinformatic analysis revealed that SNHG16 interacted with ALDH2 to exert its effects in human lung cancer cells. The expression of ALDH2 was found to be significantly ( P < 0.05 ) suppressed in human lung cancer tissues and cell lines. Overexpression of ALDH2 inhibited the proliferation and colony formation of the A549 and SK-LU-1 cells. However, silencing of ALDH2 could avoid the tumor-suppressive effects of SNHG16 knockdown. Finally, SNHG16 silencing was also found to inhibit in vivo tumor growth. Collectively, the study unveils the molecular role of SNHG16 in regulating the development of lung cancer by interacting with ALDH2.

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Long non-coding RNA SNHG16 promotes cell proliferation and invasion in lung adenocarcinoma via sponging let-7a-5p

Cited by 3 publications

References 5 publications

lncRNA-disease association prediction based on the weight matrix and projection score

lncRNA-disease association prediction based on the weight matrix and projection score

lncRNA-Disease Association Prediction Based On Weight Matrix And Projection Score

Long Noncoding RNA SNHG16 Regulates the Growth of Human Lung Cancer Cells by Modulating the Expression of Aldehyde Dehydrogenase 2 (ALDH2)

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