scholarly journals Identification of an Independent Immune-Genes Prognostic Index for Breast Cancer

2020 ◽  
Author(s):  
Deng Rong ◽  
Yu yun ◽  
Lin chen ◽  
Dong Yuxiang ◽  
Pan Yitong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Prognostic Index ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes

Abstract Objective: There are increasing evidences 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.Methods: We obtained the list of 2498 immune genes from ImmPort database. In addition, we obtained gene expression data and clinical characteristics data of BC patients 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 in normal tissues and BC tissues (p<0. 05). In 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). Through lasso regression analysis, a 15 immune genes risk scores model was established. KM survival analysisrevealed that high immune risk scores represented worse survival (p<0.001). ROC curve indicated that the immune genes risk scores model had good a 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, immune risk scoreswere proved to be an independent influencing factor for BC patients. Finally, we found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways.Conclusion:In a word, our study provides a new perspective for the expression of immune genes in BC. In addition, the immune risk scores have considerable advantages in predicting the survival of BC, which ares associated with a variety of oncogenic pathways.

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 Identification of Prognosis-Related RBPs to Reveal The Role of RNA Binding Proteins in the Progression and Prognosis of Wilms’ Tumor

2021 ◽  
Author(s):  
Xuejiao Qi ◽  
Shuyu Wang ◽  
Yihui Dong ◽  
Xiaojie Lin ◽  
Jingqiu Chen
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Prognostic Index ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
The Relationship

Abstract Background: Despite the various key functions of RBPs in posttranscriptional events, the mechanism of their influence on Wilms’ tumor has not been well elucidated. Therefore, we constructed the research to identify several RBPs related to Wilmes’ tumor progression and prognosis, for the better understanding of RBPs’ role in the occurrence and development of Wilmes’ tumor, and to provide effective reference targets for new drug development.Methods: A total of 127 samples of different clinical characteristics including gender, race and stage were selected from TARGET to carry out our study. After the gene functional enrichment pathways, univariate Cox regression analysis and lasso regression analysis were performed to test the prognostic effect of the differentially-expressed genes and establish the prognostic index . Further Cox regression analyses were utilized to identify the independence of our model and to analyze the relationship between our model and clinical parameters. What’s more, gene set enrichment analysis (GSEA) was also performed to elucidate the biological characteristics of genes involved in Wilms’ tumor. P< 0.05 was considered to be statistically significant.Results: 20 RBPs were statistically correlated with Wilms’ tumor. After the construction of a prognostic index , patients were divided into high-and low-risk scores group. Kaplan-Meier (K-M) analyses showed that patients with high risk scores possessed poorer survival probability than patients with low risk scores in both training group and test group. Furthermore, multivariate Cox regression analysis explored the relationship between our prognostic model and clinical parameters and confirmed that our model was an independent predicted factor for Wilms’ tumor. Conclusion: Our study clarifies the application of RBPs in the prognosis of Wilms’ tumor. We are confident that our risk scoring model can provide ideas for the development of new targets for broad-spectrum anticancer drugs and has great potential in clinical practice.

Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Dongqing Su ◽  
Qianzi Lu ◽  
Yi Pan ◽  
Yao Yu ◽  
Shiyuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

scholarly journals A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoping Li ◽  
Jishang Chen ◽  
Qihe Yu ◽  
Hui Huang ◽  
Zhuangsheng Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P &lt; 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

scholarly journals Identification of an Independent Immune-genes Prognostic Index for Renal Cell Carcinoma

2020 ◽  
Author(s):  
Chao Qin ◽  
Guangyao Li ◽  
Xiyi Wei ◽  
Shifeng Su ◽  
Shangqian Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model

Abstract Objective: Increasing evidence has indicated an association between immune micro-environment in clear cell renal cell carcinoma (ccRCC) and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of ccRCC patients. Methods: 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with ccRCC from the TCGA database through the R package, as well as relevant clinical information. We apply certain survival R package to analyse the survival of hub-genes before analyzing the effect of immune genes on the prognosis of clear cell renal cell carcinoma (ccRCC) utilizing Cox regression analysis. Based on the statistical correlation between hub immune gene and survival ,immune risk score model was set up.We finally constructed a nomogram to predict the survival rate of ccRCC overally. In addition, whether the immune gene risk score model is an independent prognostic factor for ccRCC is comprehensively considered applying multivariate cox regression analysis. It is worth noting that throughout the data analysis, P< 0.05 was recognized to be of significance statistically. Results: The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and ccRCC tissues (p<0. 05). Univariate cox regression analysis revealed 43 immune genes statistically correlated with ccRCC related survival risk (P<0.05). In addition, a 18-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p<0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC=0.802). Our model showed satisfying AUC and survival correlation in the validation dataset ( 5-year OS AUC=0.705, P<0.001). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of ccRCC. A nomogram was established to comprehensively predict the survival of ccRCC patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways.Conclusion: Collectively, tumor immune genes played an essential role in the prognosis of ccRCC. Furthermore, immune risk score was an independent predictive factor of ccRCC, indicating a poor survival.

scholarly journals Identification of Prognosis-related RNA Binding Proteins to Reveal the Role of RNA Binding Proteins in the Progression and Prognosis of Colon Cancer

2020 ◽  
Author(s):  
Qi Zou ◽  
Yue Ding ◽  
Yuxiang Dong ◽  
Dejun Wu ◽  
Junyi Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colon Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Binding Proteins ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model ◽  
Survival Risk

Abstract Background: RNA binding proteins (RBPs) are now under discussion as novel promising bio-markers for patients with colon cancer. The purpose of our study is to identify several RBPs related to the progression and prognosis of colon cancer, and to further investigate the mechanism of their influence on tumor progression. Methods: The transcriptome data of colon cancer as well as clinical characteristics used in this study were downloaded from the The Cancer Genome Atlas (TCGA) database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were performed to elucidate the gene functions and relative pathways. Cox and lasso regression analysis were used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P< 0.05 was considered to be statistically significant. Results: The results of the difference analysis showed that 473 RBPs exhibited differential expression between normal and colon cancer tissues (p<0. 05). Univariate cox regression analysis revealed 25 RBPs statistically correlated with colon cancer related survival risk (P<0.05). In addition, a 10-RBPs based risk scoring model was constructed through multivariate cox regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p<0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC=0.782). Our model showed satisfying AUC and survival correlation in the validation dataset (5-year OS AUC=0.744). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of colon cancer. A nomogram was established to comprehensively predict the survival of colon cancer patients with the results of multivariate cox regression analysis. Finally, we found that 10 RBPs and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion: Collectively, RBPs played an essential role in the progression and prognosis of colon cancer by regulating multiple biological pathways. Furthermore, RBPs risk score was an independent predictive factor of colon cancer, indicating a poor survival.

scholarly journals Comprehensive Analysis of Ferroptosis-Related LncRNAs in Breast Cancer Patients Reveals Prognostic Value and Relationship With Tumor Immune Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhengjie Xu ◽  
Suxiao Jiang ◽  
Juan Ma ◽  
Desheng Tang ◽  
Changsheng Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Cox Regression ◽  
Comprehensive Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is a heterogeneous malignant tumor, leading to the second major cause of female mortality. This study aimed to establish an in-depth relationship between ferroptosis-related LncRNA (FRlncRNA) and the prognosis as well as immune microenvironment of the patients with BC.Methods: We downloaded and integrated the gene expression data and the clinical information of the patients with BC from The Cancer Genome Atlas (TCGA) database. The co-expression network analysis and univariate Cox regression analysis were performed to screen out the FRlncRNAs related to prognosis. A cluster analysis was adopted to explore the difference of immune microenvironment between the clusters. Furthermore, we determined the optimal survival-related FRLncRNAs for final signature by LASSO Cox regression analysis. Afterward, we constructed and validated the prediction models, which were further tested in different subgroups.Results: A total of 31 FRLncRNAs were filtrated as prognostic biomarkers. Two clusters were determined, and C1 showed better prognosis and higher infiltration level of immune cells, such as B cells naive, plasma cells, T cells CD8, and T cells CD4 memory activated. However, there were no significantly different clinical characters between the clusters. Gene Set Enrichment Analysis (GSEA) revealed that some metabolism-related pathways and immune-associated pathways were exposed. In addition, 12 FRLncRNAs were determined by LASSO analysis and used to construct a prognostic signature. In both the training and testing sets, patients in the high-risk group had a worse survival than the low-risk patients. The area under the curves (AUCs) of receiver operator characteristic (ROC) curves were about 0.700, showing positive prognostic capacity. More notably, through the comprehensive analysis of heatmap, we regarded LINC01871, LINC02384, LIPE-AS1, and HSD11B1-AS1 as protective LncRNAs, while LINC00393, AC121247.2, AC010655.2, LINC01419, PTPRD-AS1, AC099329.2, OTUD6B-AS1, and LINC02266 were classified as risk LncRNAs. At the same time, the patients in the low-risk groups were more likely to be assigned to C1 and had a higher immune score, which were consistent with a better prognosis.Conclusion: Our research indicated that the ferroptosis-related prognostic signature could be used as novel biomarkers for predicting the prognosis of BC. The differences in the immune microenvironment exhibited by BC patients with different risks and clusters suggested that there may be a complementary synergistic effect between ferroptosis and immunotherapy.

Identification of an independent immune-genes prognostic index for breast cancer

2020 ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Chen Chen ◽  
Junyi Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
R Package ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model ◽  
Survival Risk

Abstract Objective Increasing evidence has indicated an association between immune micro-environment in breast cancer and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of breast cancer patients. Methods 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with breast cancer from the TCGA database through the R package, as well as relevant clinical information. Survival R package was applied in survival analyses for hub-genes. Cox regression analysis was used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P < 0.05 was considered to be statistically significant. Results The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and breast cancer tissues (p < 0. 05). Univariate cox regression analysis revealed 66 immune genes statistically correlated with breast cancer related survival risk, of which 30 were associated with overall survival (P < 0.05). In addition, a 15-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p < 0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC = 0.752). Our model showed satisfying AUC and survival correlation in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of breast cancer. A nomogram was established to comprehensively predict the survival of breast cancer patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion Collectively, tumor immune genes played an essential role in the prognosis of breast cancer. Furthermore, immune risk score was an independent predictive factor of breast cancer, indicating a poor survival.

scholarly journals Identification of an immune gene signature with prognostic value for patients with uterine corpus endometrial carcinoma

2020 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Fangli Yan ◽  
Yuhua Zheng
Keyword(s):  
Regression Analysis ◽  
Endometrial Carcinoma ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Signature ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a very common gynecological malignancy with a poor prognosis in the late stage. Therefore, the purpose of this study was to determine an immune-related gene signature that predicts the patients’ OS for UCEC. Methods: Based on TCGA, ImmPort, and Cistrome databases, the differential immune genes were screened and the TFs regulatory network was constructed. Functional enrichment and pathway analysis of differential immune genes were carried out. Prognostic value of 410 immune genes was analyzed by Cox regression analysis, a prognostic model was constructed, ROC curves were used to verify the accuracy of the model, and independent prognostic analysis was performed. Finally, the immune cell content was obtained by TIMER, and the correlation with the immune gene expression was evaluated by univariate Cox regression analysis. Results: It was found that the immune cell microenvironment and PI3K-Akt, MARK signaling pathways were involved in the development of UCEC. Based on the established prognostic model, ten-gene prognosis signature (PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, CBLC) for UCEC prognostic prediction were finally identified, and our study has shown that risk-score can be a powerful prognostic factor for UCEC, independent of other clinical factors. The levels of B cells and neutrophils may be significantly correlated with the patient's risk score. Conclusions: Our studies showed that the ten-gene prognosis signature had important clinical value for the prognosis of UCEC, which was helpful for individualized treatment and provided a new target for tumor immunotherapy.

scholarly journals Tumor microenvironmental prognosis analysis and molecular labeling of immune-related genes in Luminal B type breast cancer

2019 ◽  
Author(s):  
Fengjiao Gan ◽  
Kang Hu ◽  
Xiaomei Li ◽  
Ni Jiang ◽  
Qiaoyuan Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Molecular Subtype ◽  
Clinical Results ◽  
Genomic Variation ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Luminal B ◽  
The Relationship

Abstract Background: Tumor microenvironment (TME) cells is one of the important elements, constitute the tumor tissues and in predicting tumor clinical results and treatment effect has the important significance of clinical pathology. However, a detailed map for prognostic landscape of TME in Luminal B breast cancer is still lacking. Methods: ownloaded the expression profile and clinical follow-up information of luminal B breast cancer from TCGA and GEO, and used CIBERSORT to evaluate the infiltration mode of TME in 209 patients, and constructed the molecular subtype of luminal B breast cancer based on TME, further evaluated the relationship between molecular subtype and prognosis. DESeq2 was used to analyze the differentially expressed genes among molecular subtypes of luminal B breast cancer, and cox multivariate regression analysis was used for feature selection to construct TME signature. Results: ased on the median value of TMEscore, the samples were divided into High TMEscore and Low TMEscroe, and the relationship with prognosis, clinical features, immune gene expression and genomic variation was further evaluated. Different TME cells have significant correlation in luminal B breast cancer. These TME cells stratified different clinical results of luminal B breast cancer, and further TMEscore was established by Cox regression analysis. High TME score is associated with poor prognosis. Meanwhile, this study showed that CXCL10, CXCL9, GZMB and other immune activation genes and PDCD1LG2 and other immune checkpoint genes have higher expression levels in high TME score, and the mutation frequency of TP53 gene was lower in risk-H than that in risk-L. Conclusion: In this study, we systematically evaluated the TME infiltration patterns of 209 TCGA luminal B breast cancer patients and developed the TME score approach. It was found that TME score is a powerful prognostic biomarker and predictor of response to immunocheckpoint inhibitors and provides a new strategy for luminal B breast cancer immunotherapy.

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