| Objective: Breast cancer is the malignant tumor with the highest incidence in women,and it is of great significance to continuously optimize its diagnosis and treatment.The prognosis prediction for breast cancer patients includes the influence of the patient's own condition on the prognosis and the prognostic changes based on tumor genetic changes.Regarding the patient's own condition,the three receptors of estrogen receptor,progesterone receptor and human epidermal growth factor 2 have the greatest impact on the prognosis of the patient,but the change of the receptor between the primary tumor and the metastasis has the effect on the prognosis Rarely.With the continuous development of a series of technical methods such as high-throughput sequencing,the analysis based on the influence of genomics changes on the prognosis of patients is increasingly favored by researchers.Through high-throughput sequencing and bioinformatics analysis,more and more prognostic biomarkers have been discovered.Among them,due to its high tissue specificity,easy detection in body fluids such as serum,urine,tissue saliva,and stability in body fluids,more and more long-chain non-coding RNAs have been discovered,but many newly discovered lncRNAs The function is unknown,so predictions are made through bioinformatics methods and verified through biological experiments,so that more and more lncRNAs that have important functions and can guide the prognosis of breast cancer have been discovered.Methods: 1.Use clinical data of single-center breast cancer patients to explore the effect of receptor changes on the prognosis of adenocarcinoma patients;2.Use univariate analysis,cox LASSO regression and multivariate analysis to screen the prognosis of breast cancer patients through GEO data set 3.Use the CIBERSORT method to predict the proportion of interest-free infiltrating cells in the microenvironment of each patient in the GEO data set;4.Use the WGCNA method to cluster the relationship between different clinical phenotypes and gene modules;and use GO analysis to enrich Gene pathway;5.Use KM PLOTTER,TCGA database,CCLE database for external verification and expression verification;6.Use q RT-PCR experiment to detect the expression of LINC00622 in each cell line and the efficiency of siRNA and overexpression plasmid;7.Use Flow sorting to verify the changes of THP-1 cell surface antibodies;8.Use cytokine array to detect the changes of downstream cytokines after overexpression of LINC00622,and use TIMER2.0 online database and ELISA experimental verification;9.Use Lnc Locator database to predict The subcellular location of LINC00622 was tested by FISH experiment;10.The RBPDB,starbase,and Annolnc2 websites were used to jointly predict the RBP combined with LINC00622,and RPISeq was used to predict the binding ability of LINC00622 and HuR;11.Use RIP experiments to verify HuR and LINC00622,Combination of PDGFA m RNA and TGF-?1 m RNA;12.Use western blot and q RT-PCR to explore the upstream and downstream relationship between LINC00622 and HuR;13.Use MTS method to detect the effect of LINC00622 on cell proliferation changes;14.Flow Cytometry-PI staining method was used to detect the effect of LINC00622 on cell cycles;15.Transwell method was applied to detect the change of invasion and migration after altering the expression of LINC00622 and HPRT1 on cell;16.Western blot was used to detect the effect of LINC00622 on the protein level of cell EMT-related indicators.Results: 1.Different receptor status between primary tumor and metastasis affects the prognosis of patients;2.LINC00622,LINC00324,LINC00841,and HAR1 A are highly correlated with the prognosis of breast cancer patients;3.The black modules obtained by WGCNA clustering are associated with M1 type giants.Phages are highly positively correlated with basal-like subtypes;4.LINC00622 is related to immune and cytokine functions;5.Externally verified patients with high expression of LINC00622 and LINC00324 have poor prognosis;6.Online website predicts that LINC00622 has no coding ability;7.LINC00622 is lowly expressed in breast cancer and highly expressed in triple-negative breast cancer and triple-negative breast cancer cell lines;8.After THP-1is polarized into M1 macrophages,the surface CD86 expression increases,and the polarization is M2 After type macrophages,the expression of CD163 on the surface increased;9.Breast cancer cells knocked down LINC00622 and co-cultured with macrophages,and M1 type macrophages were reduced.After breast cancer cells overexpressed LINC00622,they were co-cultured with macrophages.,M1 type macrophages increase;10.LINC00622 may participate in immune regulation through cytokines;11.After overexpression of LINC00622,PDGFA and TGF-?1 factors in the cell supernatant are down-regulated;11.LINC0062 is mainly located in the cytoplasm;12.LINC00622 Can bind to HuR;13.LINC0062 acts as the upstream regulator of HuR;14.HuR can bind to PDGFA m RNA and TGF-?1 m RNA;15.LINC00622 inhibits breast cell proliferation;16.Overexpression of LINC00622 causes breast cancer cells to undergo G1 phase blockade Stagnation,knockdown LINC00622 cells increase in S phase;17.LINC00622 inhibits the migration and invasion of breast cancer cell lines;18.HuR regulated by LINC00622 is related to purine metabolism;19.HPRT1 in the purine metabolism salvage pathway is associated with poor prognosis in breast cancer patients Related;20.Knockdown HPRT1 inhibits the migration and invasion of breast cancer cells;21.HPRT1 m RNA can bind to HuR;22.LINC00622 regulates HPRT1 through HuR.Conclusion: 1.LINC00622 can be used as a prognostic indicator to effectively guide the prognosis of breast cancer patients;2.LINC00622 can regulate the content of downstream PDGFA and TGF-?1 through the RNA binding protein HuR,and regulate the polarization of M1 macrophages in the microenvironment;3.LINC00622 can inhibit the migration and invasion of breast cancer cells by adjusting the content of the rate-limiting enzyme HPRT1 in the purine metabolism salvage pathway through HuR. |
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