7 plus multi-group prediction of drug sensitivity lncRNA.

Today, I'd like to share with you an article published in June on molecular therapy nuclear acids (if: 7.032).this paper mainly focuses on the use of bioinformatics methods to identify lncrnas associated with drug response in breast cancer by integrating multi-dimensional genomic data.it is helpful to explore the mechanism of lncrna and optimize personalized drug therapy.suggestions of individual drug response related long non coding RNAs based on integrated multi omics data in toast Cancer predicts drug response related long non coding RNA based on multi-dimensional data of breast cancer. 1. Individual differences in drug response are obstacles to breast cancer (BRCA) treatment, so predicting response will help to plan treatment strategies.analysis of cancer molecular spectrum and accumulation of drug response data provide opportunities and challenges for the identification of new molecular markers in BRCA and the mechanism of tumor response to drugs.this study evaluated drug responses using a multi omics integrated system that relies on long non coding RNA (lncrna).the researchers identified lncrnas (drlncs) associated with drug response by combining lncrna, microRNA, mRNA, methylation levels, somatic mutations, and survival data of cancer patients receiving drug treatment.researchers have constructed an integrated computational multi omics method to identify drlncs of multiple chemotherapeutic drugs in BRCA.the key drlncs in adriamycin, Cytoxan and tamoxifen were identified by functional analysis, co expression network and other biological information methods. The possible mechanism of drug response of drlncs to BRCA was deduced.this study provides a framework for clinical drug response assessment based on lncrna in BRCA.2. Material method 1. Data set: TCGA (version: 07-18-2019) breast cancer mRNA, lncrna and miRNA expression profile data, DNA methylation level data, somatic cell mutation data, clinical information (including survival time and drug treatment information); gene lncrna, miRNA lncrna interaction data in raid2.0 database.2. Data processing: all the items expressed as 0 were filtered.methylation and somatic mutations were mapped to lncrna using bedtools.3. Drlncs identification: it is divided into three stages. Firstly, the difference levels of lncrna, mRNA, miRNA and methylation between patients are determined by t test, and the mutation risk score is calculated; secondly, the p value and mutation risk score in the difference level are taken as risk score; finally, the multi-dimensional hierarchical method is used to rank the risk score, and the patients with P value less than 0.05 are identified as drlncs.4. Analysis of drlncs in breast cancer: through the construction of gene co expression network, prognosis analysis, functional enrichment analysis and other bioinformatics methods and real-time quantitative PCR, drlncs in breast cancer were explored.3. Results 1. Drlncs identification of BRCA patients with multiple drugs based on multi group data integration.Figure 1. Display of individual drlncs based on multi group data integration of multiple drugs in BRCA patients. 2. By analyzing the expression level, methylation level and mutation of drlncs in drugs, the researchers found that individual drlncs showed specific characteristics for multiple drugs of BRCA patients. Figure 2. The specificity of individual drlncs in BRCA patients 3. Construction of drlncs gene co expression network related to drug response in BRCA Figure 3. Drlncs coexpression network in breast cancer 4. In order to evaluate the potential value of drlncs as a prognostic biomarker of BRCA, the researchers identified drlncs that were significantly related to the survival of patients in a variety of drugs. Figure 4. Survival related drlncs5 of breast cancer, based on the results of functional enrichment analysis of all drlncs of multiple drugs, the researchers inferred the possible mechanism of key drlncs. Figure 5. The role and mechanism of drlncs in different drugs of BRCA. 6. Real time quantitative PCR analysis showed that the expression of hoxa-as2 in adriamycin treated patients was significantly higher than that in patients without chemotherapy. compared with normal BRCA cells, adriamycin resistant BRCA cells showed higher colony forming ability after treated with 5 mg / L adriamycin. [br / > Figure 6. Hoxa-as2 can promote adriamycin resistance in BRCA cells. 4. Conclusion this paper integrates the multi-dimensional data of TCGA, lncrna, miRNA, mRNA expression, DNA methylation, somatic mutation, survival information and drug response information. based on the lncrna interaction confirmed by experiments, drlncs related to chemotherapeutic drug reactions were identified in breast cancer by bioinformatics calculation. the drlncs were characterized by co expression network, functional enrichment and other bioinformatics methods. using public data, combined with experimental analysis. the advantage lies in the integration and analysis of multi-dimensional omics data, and the unique mining of TCGA clinical information effectively identifies the key drlncs in breast cancer, which provides guidance for clinical treatment. if you have analysis needs, you can also add wechat communication with little sister