The Role of Drug Resistance Causing lncRNAs in Breast Cancer: In-silico Analysis

2021 ◽  
Author(s):  
Amirhossein Hajialiasgary Najafabadi ◽  
Mahdieh Khojasteh ◽  
Kamran Ghaedi
Keyword(s):  
Breast Cancer ◽  
Drug Resistance ◽  
Roc Curve ◽  
Drug Response ◽  
Cox Regression ◽  
Patient Survival ◽  
Biological Pathways ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Breast cancer is the most common cancer in women globally. LncRNAs are non-coding RNAs that play an essential role in biological pathways. Many lncRNAs have been discovered to influence cancer medication resistance. As a result, identifying how lncRNAs may cause drug resistance is vital.Method: Breast cancer TCGA RNA-seq data was applied in this study. We used the PharmacoGX package to explore lncRNAs with drug resistance or sensitivity effect through GDSC and CCLE data. Differential gene expression analysis (DGE) was used to find dysregulated lncRNAs (P<0.01). Survival analysis was performed to identify lncRNAs associated with patient survival, and a model based on them was developed. Multivariate cox regression analysis and ROC curve analysis were applied to assess the model. The TCGA-BRCA and two independent datasets (GSE21653 and GSE20685) were used to study the relevance of lncRNAs in biological pathways. lncRNA-miRNA-mRNA interaction network was investigated. The connections of lncRNAs with MRPs were analysed through the correlation test. Finally, lncRNA and MRP mRNAs attachment sites were analysed through the LncRRisearch tool.Result: According to our data, thirty-eight lncRNAs were associated with cell drug response in breast cancer cells. IL12A-AS1, AC137723.1, LINC00667, SVIL-AS1, CYTOR, and MIR4435-2HG linked to patient survival (P<0.05). AC137723.1 and LINC00667 were identified as good prognostic genes, while the others were discovered to have poor prognostic effects. Moreover, the risk score model separated patients perfectly, in which about 45% of high-risk patients were dead; by contrast, around 95% of low-risk patients could survive. ROC curve results proved that CYTOR, MIR4435-2HG, and LINC00667 are potential biomarkers in breast cancer with AUC >0.8. Pathway analysis revealed that CYTOR and MIR4435-2HG are highly correlated with the Epithelial-Mesenchymal transition pathway, while AC137723.1 and LINC00667 were negatively correlated with the pathway. AC128688.2, CYTOR, TDRKH-AS1 and LINC00667 can participate in lncRNA-miRNA-mRNA networks. Also, MIR22HG might influence drug resistance by attaching to MRP mRNAs.Conclusion: Our findings revealed 38 lncRNAs involved in cancer cell treatment resistance and sensitivity. They can participate in patients’ prognosis, diagnosis and cellular pathways. Also, they may influence cell drug response through connections with CSPs, lncRNA-miRNA-mRNA networks and MRPs.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

Clinical Outcome With Correlation to Disseminated Tumor Cell (DTC) Status After DTC-Guided Secondary Adjuvant Treatment With Docetaxel in Early Breast Cancer

2014 ◽  
Vol 32 (34) ◽  
pp. 3848-3857 ◽  
Author(s):  
Bjørn Naume ◽  
Marit Synnestvedt ◽  
Ragnhild Sørum Falk ◽  
Gro Wiedswang ◽  
Kjetil Weyde ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Adjuvant Treatment ◽  
Cox Regression ◽  
Surrogate Marker ◽  
Disseminated Tumor Cells ◽  
Rank Test ◽  
Cox Regression Analysis ◽  
Risk Patients

Purpose The presence of disseminated tumor cells (DTCs) in bone marrow (BM) predicts survival in early breast cancer. This study explores the use of DTCs for identification of patients insufficiently treated with adjuvant therapy so they can be offered secondary adjuvant treatment and the subsequent surrogate marker potential of DTCs for outcome determination. Patients and Methods Patients with early breast cancer who had completed six cycles of adjuvant fluorouracil, epirubicin, and cyclophosphamide (FEC) chemotherapy underwent BM aspiration 2 to 3 months (BM1) and 8 to 9 months (BM2) after FEC. Presence of DTCs in BM was determined by immunocytochemistry using pan-cytokeratin monoclonal antibodies. If one or more DTCs were present at BM2, six cycles of docetaxel (100 mg/m2, once every 3 weeks) were administered, followed by DTC analysis 1 and 13 months after the last docetaxel infusion (after treatment). Cox regression analysis was used to evaluate disease-free interval (DFI). Results Of 1,066 patients with a DTC result at BM2 and available follow-up information (median follow-up, 71.9 months from the time of BM2), 7.2% were DTC positive. Of 72 docetaxel-treated patients analyzed for DTCs after treatment, 15 (20.8%) had persistent DTCs. Patients with remaining DTCs had markedly reduced DFI (46.7% experienced relapse) compared with patients with no DTCs after treatment (adjusted hazard ratio, 7.58; 95% CI, 2.3 to 24.7). The docetaxel-treated patients with no DTCs after treatment had comparable DFI (8.8% experienced relapse) compared with those with no DTCs both at BM1 and BM2 (12.7% experienced relapse; P = .377, log-rank test). Conclusion DTC status identifies high-risk patients after FEC chemotherapy, and DTC monitoring status after secondary treatment with docetaxel correlated strongly with survival. This emphasizes the potential for DTC analysis as a surrogate marker for adjuvant treatment effect in breast cancer.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Wenqing Zhou ◽  
Yongkui Pang ◽  
Yunmin Yao ◽  
Huiying Qiao
Keyword(s):  
Breast Cancer ◽  
Gene Ontology ◽  
Regression Analysis ◽  
Pathway Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Patient Survival ◽  
Receptor Interaction ◽  
Cox Regression Analysis

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p<0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and AGRs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

scholarly journals Clinical Identification of Dysregulated Circulating microRNAs and Their Implication in Drug Response in Triple Negative Breast Cancer (TNBC) by Target Gene Network and Meta-Analysis

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 549
Author(s):  
Amal Qattan ◽  
Taher Al-Tweigeri ◽  
Wafa Alkhayal ◽  
Kausar Suleman ◽  
Asma Tulbah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Resistance ◽  
Triple Negative Breast Cancer ◽  
Drug Response ◽  
Triple Negative ◽  
Meta Analysis ◽  
Integrated Analysis ◽  
Circulating Mirnas ◽  
Persistent Problem ◽  
Network Analyses

Resistance to therapy is a persistent problem that leads to mortality in breast cancer, particularly triple-negative breast cancer (TNBC). MiRNAs have become a focus of investigation as tissue-specific regulators of gene networks related to drug resistance. Circulating miRNAs are readily accessible non-invasive potential biomarkers for TNBC diagnosis, prognosis, and drug-response. Our aim was to use systems biology, meta-analysis, and network approaches to delineate the drug resistance pathways and clinical outcomes associated with circulating miRNAs in TNBC patients. MiRNA expression analysis was used to investigate differentially regulated circulating miRNAs in TNBC patients, and integrated pathway regulation, gene ontology, and pharmacogenomic network analyses were used to identify target genes, miRNAs, and drug interaction networks. Herein, we identified significant differentially expressed circulating miRNAs in TNBC patients (miR-19a/b-3p, miR-25-3p, miR-22-3p, miR-210-3p, miR-93-5p, and miR-199a-3p) that regulate several molecular pathways (PAM (PI3K/Akt/mTOR), HIF-1, TNF, FoxO, Wnt, and JAK/STAT, PD-1/PD-L1 pathways and EGFR tyrosine kinase inhibitor resistance (TKIs)) involved in drug resistance. Through meta-analysis, we demonstrated an association of upregulated miR-93, miR-210, miR-19a, and miR-19b with poor overall survival outcomes in TNBC patients. These results identify miRNA-regulated mechanisms of drug resistance and potential targets for combination with chemotherapy to overcome drug resistance in TNBC. We demonstrate that integrated analysis of multi-dimensional data can unravel mechanisms of drug-resistance related to circulating miRNAs, particularly in TNBC. These circulating miRNAs may be useful as markers of drug response and resistance in the guidance of personalized medicine for TNBC.

scholarly journals The Investigation of Associations between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344, EGFR rs2227983 Polymorphisms and Breast Cancer Phenotype and Prognosis

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1419
Author(s):  
Justina Bekampytė ◽  
Agnė Bartnykaitė ◽  
Aistė Savukaitytė ◽  
Rasa Ugenskienė ◽  
Erika Korobeinikova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Cancer Prognosis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Cox Regression Analysis ◽  
Pcr Rflp ◽  
Oncological Diseases ◽  
Cancer Phenotype

Breast cancer is one of the most common oncological diseases among women worldwide. Cell cycle and apoptosis—related genes TP53, BBC3, CCND1 and EGFR play an important role in the pathogenesis of breast cancer. However, the roles of single nucleotide polymorphisms (SNPs) in these genes have not been fully defined. Therefore, this study aimed to analyze the association between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344 and EGFR rs2227983 polymorphisms and breast cancer phenotype and prognosis. For the purpose of the analysis, 171 Lithuanian women were enrolled. Genomic DNA was extracted from peripheral blood; PCR-RFLP was used for SNPs analysis. The results showed that BBC3 rs2032809 was associated with age at the time of diagnosis, disease progression, metastasis and death. CCND1 rs9344 was associated with tumor size, however an association resulted in loss of significance after Bonferroni correction. In survival analysis, significant associations were observed between BBC3 rs2032809 and OS, PFS and MFS. EGFR rs2227983 also showed some associations with OS and PFS (univariate Cox regression analysis). However, the results were in loss of significance (multivariate Cox regression analysis). In conclusion, BBC3 rs2032809 polymorphism was associated with breast cancer phenotype and prognosis. Therefore, it could be applied as potential markers for breast cancer prognosis.

scholarly journals Calumenin contributes to epithelial-mesenchymal transition and predicts poor survival in glioma

2021 ◽  
Vol 12 (1) ◽  
pp. 67-75
Author(s):  
Ying Yang ◽  
Jin Wang ◽  
Shihai Xu ◽  
Fei Shi ◽  
Aijun Shan
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Malignant Gliomas ◽  
Strongly Correlated ◽  
Who Grade ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
R Language ◽  
Biological Adhesion ◽  
Tcga Dataset

Abstract Background Calumenin (CALU) has been reported to be associated with invasiveness and metastasis in some malignancies. However, in glioma, the role of CALU remains unclear. Methods Clinical and transcriptome data of 998 glioma patients, including 301 from CGGA and 697 from TCGA dataset, were included. R language was used to perform statistical analyses. Results CALU expression was significantly upregulated in more malignant gliomas, including higher grade, IDH wildtype, mesenchymal, and classical subtype. Gene Ontology analysis revealed that CALU-correlated genes were mainly enriched in cell/biological adhesion, response to wounding, and extracellular matrix/structure organization, all of which were strongly correlated with the epithelial-mesenchymal transition (EMT) phenotype. GSEA further validated the profound involvement of CALU in EMT. Subsequent GSVA suggested that CALU was particularly correlated with three EMT signaling pathways, including TGFβ, PI3K/AKT, and hypoxia pathway. Furthermore, CALU played synergistically with EMT key markers, including N-cadherin, vimentin, snail, slug, and TWIST1. Survival and Cox regression analysis showed that higher CALU predicted worse survival, and the prognostic value was independent of WHO grade and age. Conclusions CALU was correlated with more malignant phenotypes in glioma. Moreover, CALU seemed to serve as a pro-EMT molecular target and could contribute to predict prognosis independently in glioma.

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