| Thyroid cancer is the most common endocrine malignancy and usually arises from parafollicular or follicular thyroid cells.The global incidence of thyroid cancer has continued to increase over the past few decades.The occurrence and development of thyroid cancer are affected by many factors such as genetics and environment.With the advent of high-throughput sequencing methods,the role of non-codingRNAs in thyroid cancer has been discovered.As non-codingRNAs,lncRNAs lack the ability to encode proteins and function by modifying and regulating gene expression.By analyzing existing databases,we obtained differentially expressed lncRNAs and screened the best diagnostic biomarkers for thyroid cancer.Among them,LBX2-AS1 is one of most differenetly expressed genes and has been reported to be highly expressed in thyroid cancer,and can promote the proliferation and metastasis of thyroid cancer cells.Our preliminary experiments have found that LBX2-AS1 can also promot glycolysis and cell cycle,which have not yet been reported previously and may be a new mechanism underlying.In this study,the gene expression data of thyroid cancer tissue and adjacent normal tissue will be integrated and analyzed by bioinformatics technology.The function of LBX2-AS1 will be studied and its underlying mechanisms would also be explored.This study will provide new biomarkers and evidences for diagnosis and treatment of thyroid cancer.This research is divided into the following four parts.Part One Screening of thyroid cancer biomarkers based on bioinformatics analysisObjective: To screen the differentially expressed genes of thyroid cancer through bioinformatics,and to search for the best diagnostic biomarkers.Methods:1.Gene expression data and biological information of thyroid cancer patients were downloaded from the TCGA database,and differentially expressed genes in cancer tissues and adjacent tissues were analyzed.2.Machine learning was applied for optimal biomarkers screening.3.R language was used to establish RF model,DT model and SVM model,and the diagnostic ability of the three classification prediction models were evaluated.4.lncRNA-miRNA-mRNA interaction networks were constructed.Results:1.A total of 107 differential lncRNAs were screened,which included 79 lncRNAs up-regulated and 28 lncRNAs down-regulated.A total of 81 differential miRNAs were screened,of which 49 were up-regulated and 32 were down-regulated.A total of 515 mRNAs were identified,including 339 mRNAs up-regulated and 176 mRNAs down-regulated.2.11 differentially expressed lncRNAs were screened out as optimal biomarkers,among which 8 were downregulated including ADD3-AS1,MIR100 HG,FAM95C MORC2-AS1,LINC00506,ST7-AS1,LOC339059,FAM181A-AS1,and 3 were upregulated including MIR181A2 HG,LBX2-AS1 and BLACAT1.6 miRNAs were chose as optimal biomarkers,including 3 down-regulated miRNAs including hsa-miR-9-5p,hsa-miR-199b-5p,and hsa-miR-214-3p,and 3 up-regulated ones including hsa-miR-146b-3p,miR-4709-3p and hsa-miR-34a-5p3.For the 11 lncRNAs screened,RF,DT,and SVM were used to construct prediction models respectively.The AUC values in the RF,DT,and SVM models were 99.2%,92.2%,and 99.0%,respectively.4.ceRNA networks of lncRNA-miRNA-mRNA were constructed,which contains 11 diagnostic lncRNAs,6 diagnostic miRNAs and 95 differentially expressed mRNAs.Summary: By analyzing the differentially expressed lncRNAs and miRNAs in thyroid cancer tissues and adjacent tissues in TCGA database,11 lncRNAs and 6 miRNAs were screened out,which might be the promising diagnostic biomarkers for thyroid cancer.Part Two The expression of lncRNA LBX2-AS1 in thyroid cancer tissue and its correlation with clinical parametersObjective: To clarify the expression of lncRNA LBX2-AS1 in thyroid cancer tissue and its correlation with clinical parametersMethods:1.Clinical tissue samples and information were collected from patients with thyroid cancer.2.The expression of LBX2-AS1 in thyroid cancer tissue and its adjacent tissue was detected by real-time PCR.The expression of PKM2 in thyroid cancer tissues and their adjacent tissues were detected by immunohistochemistry method.3.The relationship between the expression of LBX2-AS1 in thyroid cancer tissue and the clinicopathological characteristics of patients was analyzed.And the correlation between the expression of LBX2-AS1 and PKM2 in thyroid cancer tissue was also analyzed.Results:1.Compared with that in adjacent tissues,the expression of LBX2-AS1 in thyroid cancer tissues was significantly increased(P<0.05).2.The expression of LBX2-AS1 was not significantly correlated with gender,age and tumor size(all P>0.05),,but was correlated with lymph node metastasis and tumor stage(all P<0.05).Patients with high LBX2-AS1 expression levels were more likely to have lymph node metastasis(χ2=5.853,P<0.05),and higher tumor stage(χ2=3.903,P<0.05)3.The expression of LBX2-AS1 was positively correlated with the expression of PKM2(R~2=0.479,P<0.05)Summary: The expression of LBX2-AS1 in thyroid cancer tissues was significantly higher than that in adjacent tissues.The expression level of LBX2-AS1 was positively correlated with the expression level of PKM2.Part Three The role of lncRNA LBX2-AS1 in thyroid cancer cellsObjective: To explore the role of LBX2-AS1 in thyroid cancer cellsMethods:1.TPC-1 cells with LBX2-AS1 knockdown was constructed.2.The effect of LBX2-AS1 knockdown on the proliferation of TPC-1cells was detected by MTS.3.The effect of LBX2-AS1 knockdown on the invasion and migration ability of TPC-1 cells was detected by Transwell assay.4.The effects of LBX2-AS1 knockdown on TPC-1 cell cycle were detected by flow cytometry.5.The effect of LBX2-AS1 knockdown on the glycolysis of TPC-1cells was detected by hippocampal assay.Results:1.Compared with that of control group,the proliferation of TPC-1 cells was inhibited significantly by LBX2-AS1 knockdown(P<0.05).2.Compared with the control group,LBX2-AS1 knockdown led to significantly inhibited abilities of invasion and migration of TPC-1 cells(P<0.05).3.Compared with control group,LBX2-AS1 knockdown could significantly inhibit the cell cycle of TPC-1 cells,resulting in significantly increased proportion of cells in G1 phase and decreaed proportion of cells in S phase and G2 phase(P<0.05).4.Compared with control group,LBX2-AS1 knockdown could significantly inhibit the glycolytic capacity of TPC-1 cells(P<0.05).Summary: LBX2-AS1 knockdown in thyroid cancer cells can reduce glycolysis and inhibit cell cycle,resulting in cell cycle arrest in G1 phase.Part Four lncRNA LBX2-AS1 promoting thyroid cancer progression by enhancing aerobic glycolysis and the mechanism underlying Artemisinin anti-cancer effectsObjective: To clarify the mechanism of LBX2-AS1 enhancing aerobic glycolysis,and to explore the possibility of Artemisinin in the treatment of thyroid cancer.Methods:1.Real-time PCR and Western blot were used to detect the expression of downstream target genes of LBX2-AS1..2.To clarigy the effect of LBX2-AS1 on miR-491-5p/PKM2 by dual-luciferase reporter analysis.3.To verify the effect of Artemisinin on glycolysis in thyroid cancer cells by hippocampal experiment.Results:1.Compared with the control group,LBX2-AS1 knockdown in TPC-1cells caused significantly reduced expression of cyclin D1,HIF-1α and PKM2 at mRNA and protein levels(all P<0.05)2.The luciferase activity of psi CHECK2 containing LBX2-AS1 and PKM2 mRNA was inhibited by miR-491-5p mimic,respectively.The reduced gene expression of cyclin D1,HIF-1α and PKM2 could be partly recovered by miR-491-5p inhibition.LBX2-AS1 could regulate PKM2 expression by binding to miR-491-5p as a molecular sponge,thereby regulating the expression of cyclin D1 and HIF-1α.3.Artemisinin could reduce the glycolysis of thyroid cancer cells by promoting the degradation of HIF-1α protein.Summary: LBX2-AS1 promotes aerobic glycolysis in thyroid cancer cells through the miR-491-5p/PKM2 pathway.Artemisinin may inhibit glycolysis in TPC-1 cells.Conclusions:1.LBX2-AS1 can be used as a biomarker for thyroid cancer progression.2.LBX2-AS1 promotes the glycolysis of thyroid cancer cells through miR-491-5p/PKM2 pathway.3.Artemisinin inhibits glycolysis of thyroid cancer cells by directly targeting HIF-1α degradation. |
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