Long non‑coding RNAs in small cell lung cancer: A potential opening to combat the disease (Review)

Li, Tian‐Tian; He, Rong‐Quan; Ma, Jie; Li, Zu‐Yun; Hu, Xiaohua; Chen, Gang

doi:10.3892/or.2018.6635

Cited by 9 publications

(6 citation statements)

References 99 publications

(119 reference statements)

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“…Above all, lncRNAs could affect tumorigenesis via gene modulation. Moreover, various cancers including SCLC are functionally associated with abnormally expressed lncRNAs [16,17]. For example, highly expressed AFAP1-AS1 is correlated with nasopharyngeal carcinoma metastasis [18].…”

Section: Introductionmentioning

confidence: 99%

YY1-induced lncRNA ZFPM2-AS1 facilitates cell proliferation and invasion in small cell lung cancer via upregulating of TRAF4

et al. 2020

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Background: Newly identified lncRNA zinc finger protein, FOG family member 2 antisense RNA 1 (ZFPM2-AS1) is identified as an oncogenic gene. However, the role of ZFPM2-AS1 in small cell lung cancer (SCLC) is poorly comprehended. Methods: The expression of genes in SCLC tissues and cells was measured by qRT-PCR. Colony formation, EdU, CCK-8, transwell and wound healing as well as in vivo assays revealed the function of ZFPM2-AS1 in SCLC. ChIP, luciferase reporter, RIP and RNA pull down assays demonstrated the binding relation among genes. Results: ZFPM2-AS1 was significantly upregulated in SCLC tissues and cells. ZFPM2-AS1 deficiency attenuated SCLC cell proliferation, invasion and migration. In addition, ZFPM2-AS1 was transcriptionally activated by Yin Yang 1 (YY1) factor. Further, miR-3612 was confirmed as downstream miRNA of ZFPM2-AS1. Moreover, TNF receptor associated factor 4 (TRAF4) was the target gene of miR-3612 in SCLC. ZFPM2-AS1, miR-3612 and TRAF4 jointly constituted a competing endogenous RNA (ceRNA) network in SCLC. Finally, TRAF4 could countervail ZFPM2-AS1 downregulationmediated function on SCLC cell proliferation and invasion in vitro and tumor growth in vivo. Conclusion: Our study elucidated the oncogenic effect of ZFPM2-AS1 in SCLC progression, indicating it may be a therapeutic target for SCLC.

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Section: Introductionmentioning

confidence: 99%

YY1-induced lncRNA ZFPM2-AS1 facilitates cell proliferation and invasion in small cell lung cancer via upregulating of TRAF4

et al. 2020

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“…LncRNA plays an important role in the biological process of chromatin modification, transcription and post-transcription regulation and so on [4], and has a very important biological function. The variation or dysfunction of lncRNA can lead to the occurrence of many diseases, such as lung cancer [5][6][7][8]、breast cancer [9][10]、 prostate cancer [11][12] 、 osteosarcoma [13] 、 colorectal cancer [14] 、 gastric cancer [15] 、 bladder cancer [16] and cervical cancer [17], etc. Therefore, the research on lncRNA-disease association prediction can not only deepen the understanding of the pathogenic mechanism for complex diseases at the molecular level, but also use lncRNA as disease diagnosis, predicted biological target, drug target for treatment and prevention.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Regression Analysis for lncRNA-Disease Association Prediction Based on Pathological Stage Data

Wang

Zhang

2021

IEEE Access

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Accumulating researches have found that lncRNAs play a key role in many important biological processes, such as chromatin modification, transcription, and post-transcription regulation. Because lncRNAs play an important role in the life process, many important complex diseases have been linked to the variation and dysfunction of lncRNAs. In current prediction researches on lncRNA-disease association, clinical prognosis information of the disease (such as pathological stage, clinical stage and so on) is rarely mentioned. In this manuscript, we apply the pathological stage data into the lncRNA-disease association prediction. Firstly, coordinates reverse rotation in circular (CRRC) is proposed. 6 clusters are calculated by the proposed cluster generating algorithm (ClGeA) based on CRRC. Secondly, harmonic importance ranking (HIR) is put forward. 28 core variables are obtained by the proposed selection algorithm of core variables for cancer pathological stage (SA-CV-CPS) based on HIR and cluster. Finally, on the basis of the above 28 core variables, pathological stage prediction algorithm for lncRNA-disease association based on principal component regression analysis (PSPA-LA-PCRA) is developed. Through PSPA-LA-PCRA, principal component set (including 20 PCs) and prediction model are gained. The proposed prediction model is based on unknown human lncRNA-disease association combining with the pathological stage data. Experimental results show that better results for AUC, precision rate, recall rate and F1-score of the prediction model are achieved by PSPA-LA-PCRA, which provides a favorable research premise for subsequent prediction studies of lncRNA-disease associations.

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“…Recent advancements in high-throughput gene sequencing analysis have led to the identification of a large number of differentially expressed long non-coding RNAs (lncRNAs) in the progression of various cancers (Jing et al, 2018;Li et al, 2018;Malih, Saidijam & Malih, 2016). A growing number of studies have reported that dysregulated lncRNAs in HCC are related to chronic HBV or HCV infection, histological type, TNM stage, lymph node metastasis, and prognosis Motawi et al, 2019;Zheng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Integrative analysis of dysregulated lncRNA-associated ceRNA network reveals potential lncRNA biomarkers for human hepatocellular carcinoma

Zhang

Yang

et al. 2020

PeerJ

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Background Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis and a high incidence. The molecular changes and novel biomarkers of HCC need to be identified to improve the diagnosis and prognosis of this disease. We investigated the current research concentrations of HCC and identified the transcriptomics-related biomarkers of HCC from The Cancer Genome Atlas (TGCA) database. Methods We investigated the current research concentrations of HCC using literature metrology analysis for studies conducted from 2008 to 2018. We identified long noncoding RNAs (lncRNAs) that correlated with the clinical features and survival prognoses of HCC from The Cancer Genome Atlas (TGCA) database. Differentially expressed genes (lncRNAs, miRNAs, and mRNAs) were also identified by TCGA datasets in HCC tumor tissues. A lncRNA competitive endogenous RNA (ceRNA) network was constructed from lncRNAs based on intersected lncRNAs. Survival times and the association between the expression levels of the key lncRNAs of the ceRNA network and the clinicopathological characteristics of HCC patients were analyzed using TCGA. Real-time polymerase chain reaction (qRT-PCR) was used to validate the reliability of the results in tissue samples from 20 newly-diagnosed HCC patients. Results Analysis of the literature pertaining to HCC research revealed that current research is focused on lncRNA functions in tumorigenesis and tumor development. A total of 128 HCC dysregulated lncRNAs were identified; 66 were included in the co-expressed ceRNA network. We analyzed survival times and the associations between the expression of 66 key lncRNAs and the clinicopathological features of the HCC patients identified from TCGA. Twenty-six lncRNAs were associated with clinical features of HCC (P < 0.05) and six key lncRNAs were associated with survival time (log-rank test P < 0.05). Six key lncRNAs were selected for the validation of their expression levels in 20 patients with newly diagnosed HCC using qRT-PCR. Consistent fold changes in the trends of up and down regulation between qRT-PCR validation and TCGA proved the reliability of our bioinformatics analysis. Conclusions We used integrative bioinformatics analysis of the TCGA datasets to improve our understanding of the regulatory mechanisms involved with the functional features of lncRNAs in HCC. The results revealed that lncRNAs are potential diagnostic and prognostic biomarkers of HCC.

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Long non‑coding RNAs in small cell lung cancer: A potential opening to combat the disease (Review)

Cited by 9 publications

References 99 publications

YY1-induced lncRNA ZFPM2-AS1 facilitates cell proliferation and invasion in small cell lung cancer via upregulating of TRAF4

YY1-induced lncRNA ZFPM2-AS1 facilitates cell proliferation and invasion in small cell lung cancer via upregulating of TRAF4

Principal Component Regression Analysis for lncRNA-Disease Association Prediction Based on Pathological Stage Data

Integrative analysis of dysregulated lncRNA-associated ceRNA network reveals potential lncRNA biomarkers for human hepatocellular carcinoma

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