scholarly journals Immune-related Gene Data-based Molecular Subtyping Related to the Prognosis for Breast Cancer Patients

2020 ◽  
Author(s):  
Guoyu Mu ◽  
Hong Ji ◽  
Hui He ◽  
Hongjiang Wang
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Prognosis Prediction ◽  
Patient Prognosis ◽  
Prognosis Prediction Model ◽  
Immune Related Genes ◽  
Accuracy And Stability

Abstract Background Breast cancer (BC), the most frequently seen malignant tumor in female, is associated with increasing morbidity and mortality year by year. Generally, the available treatments for BC include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular targeted therapy. Typically, as molecular biology, immunology and pharmacogenomics develop, a growing amount of evidence has suggested that immunocyte infiltration into tumor microenvironment, together with the immunophenotype of tumor cells, would remarkably influence the development and malignant transformation of tumor; as a result, immunotherapy has become a promising therapy for treating BC, which would also affect patient prognosis.Methods In this study, samples collected from TCGA and ImmPort database would be analyzed to search for specific immune-related genes affecting BC patient prognosis. A total of 64 immune-related genes with significant correlation with patient prognosis had been screened and performed shrinkage estimate, among which, 29 most representative ones with significant correlation with patient prognosis had been selected and utilized to establish the prognosis prediction model for BC patients (as referred to as the RiskScore equation). Thereafter, samples in both training set and test set would be substituted into the model, respectively; meanwhile, BC patients would also be divided based on the median RiskScore to assess the efficiency, accuracy and stability of the model in predicting and classifying patient prognosis. Subsequently, functional annotations, GO and KEGG signaling pathway enrichment analysis would be carried out among the 29 as-screened immune-related genes.Results The results found that, these 29 genes could be mainly enriched to numerous BC- and immune microenvironment-related pathways. Eventually, the relationship between RiskScore and the sample clinical features as well as the signaling pathways would be analyzed.Conclusions Our findings indicate that, the prognosis prediction model RiskScore established on the basis of the expression profiles of 29 immune-related genes has displayed high prediction accuracy and stability in identifying the immune features, which can guide the clinicians to diagnose and predict the prognosis for different immunophenotypes, in the meantime of offering numerous therapeutic targets for precisely treating BC in clinic using the identified subtype-specific immune molecules.

scholarly journals Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

Breast Cancer ◽  
2020 ◽  
Author(s):  
Guoyu Mu ◽  
Hong Ji ◽  
Hui He ◽  
Hongjiang Wang
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Tumor Development ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Prognostic Prediction ◽  
Immune Related Genes ◽  
Accuracy And Stability

Abstract Background Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis. Methods In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed. Results We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed. Conclusions Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.

scholarly journals Immune Gene Data-based Molecular Subtyping of Rectal Adenocarcinoma Related to Prognosis

2020 ◽  
Author(s):  
Xia shu sen ◽  
Hong-peng Tian ◽  
Zuo-liang Liu ◽  
Zai-hua Yan ◽  
Xian-yan Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Rectal Adenocarcinoma ◽  
Enrichment Analysis ◽  
Close Correlation ◽  
Pathway Enrichment Analysis ◽  
Immune Gene ◽  
Patient Prognosis ◽  
Prognostic Prediction ◽  
Immune Related Genes ◽  
Gene Data

Abstract Background: The morbidity and mortality of rectal adenocarcinoma (READ) is increasing, which is considered as an aggressive type of colorectal malignancy. A great deal of evidence has suggested the significant association between the progression of READ and the immunophenotype of tumor cells (i.e. the expression of intracellular immune-related genes).Methods: Samples retrieved from the TCGA and ImmPort database were investigated to identify immune-related genes specifically impacting the prognosis of READ patients. Several typical ones were then selected to construct the prognostic prediction model of READ patients through Lasso algorithm. The training and test cohorts were incorporated into the model, respectively. We stratified READ patients to evaluate the accuracy, efficiency and stability of the model in the prediction and classification of patient prognosis according to the value of median RiskScore (Risk-H and Risk-L). GO and KEGG signaling pathway enrichment analysis were conducted among the nine selected immune-related genes.Results: A total of 57 immune-related displaying marked correlated with patient prognosis were identified and nine most typical genes could be majorly enriched into several pathways with close correlation with READ and the corresponding immune response. the distribution of nine immune-related genes were examined in the samples from both Risk-H and –L groups. Finally, the connection of the RiskScore value with the clinical characteristics of sample and the related signaling pathways were investigated.Conclusions: The prognostic prediction model of RiskScore constructed based on the expression profiling of the nine immune-associated genes exhibited high prediction accuracy and stability to identify the relevant immune features. This model could contribute to the guidance for clinicians in diagnosing and predicting the prognosis for various immunophenotypes. Meanwhile, it also can offer various therapeutic targets for precise treatment of READ in clinical practice according to the identified immune molecules specific to different subtypes.

scholarly journals Risk factors for pegylated liposomal doxorubicin-induced moderate to severe hand-foot syndrome in breast cancer patients: assessment of baseline clinical parameters

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guohua Liang ◽  
Wenjie Ma ◽  
Yanfang Zhao ◽  
Eryu Liu ◽  
Xiaoyu Shan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Prediction Model ◽  
Cancer Patients ◽  
Blood Circulation ◽  
Pegylated Liposomal Doxorubicin ◽  
Dose Intensity ◽  
Breast Cancer Patients ◽  
Hand Foot Syndrome ◽  
Peripheral Blood Circulation

Abstract Background Hand-foot syndrome (HFS) is a side effect of skin related to pegylated liposomal doxorubicin (PLD) application. Moderate to severe hand-foot syndrome (MSHFS) might have a serious impact on patients’ quality of life and treatment. However, information on risk factors for the development of MSHFS is still limited. To analyze the risk factors for PLD-induced MSHFS in breast cancer patients and constructed a logistic regression prediction model. Methods We conducted a retrospective analysis of breast cancer patients who were treated with a PLD regimen in the Tumor Hospital of Harbin Medical University from January 2017 to August 2019. A total of 26 factors were collected from electronic medical records. Patients were divided into MSHFS (HFS > grade 1) and NMHFS (HFS ≤ grade 1) groups according to the NCI classification. Statistical analysis of these factors and the construction of a logistic regression prediction model based on risk factors. Results A total of 44.7% (206/461) of patients developed MSHFS. The BMI, dose intensity, and baseline Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST) levels in the MSHFS group, as well as good peripheral blood circulation, excessive sweat excretion, history of gallstones, and tumour- and HER2-positive percentages, were all higher than those in the NMHFS group (P < 0.05). The model for predicting the occurrence of MSHFS was P = 1/1 + exp. (11.138–0.110*BMI-0.234*dose intensity-0.018*baseline ALT+ 0.025*baseline AST-1.225*gallstone history-0.681* peripheral blood circulation-1.073*sweat excretion-0.364*with or without tumor-0.680*HER-2). The accuracy of the model was 72.5%, AUC = 0.791, and Hosmer-Lemeshow fit test P = 0.114 > 0.05. Conclusions Nearly half of the patients developed MSHFS. The constructed prediction model may be valuable for predicting the occurrence of MSHFS in patients.

scholarly journals Cytotoxic T-lymphocyte infiltration and chemokine predict long-term patient survival independently of tumor mutational burden in triple-negative breast cancer

2021 ◽  
Vol 13 ◽  
pp. 175883592110066
Author(s):  
Eriko Katsuta ◽  
Li Yan ◽  
Mateusz Opyrchal ◽  
Pawel Kalinski ◽  
Kazuaki Takabe
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Patient Survival ◽  
Cytotoxic T Lymphocyte ◽  
Breast Cancer Patients ◽  
Mutational Burden ◽  
Cancer Immunity ◽  
Anti Cancer ◽  
Tumor Mutational Burden ◽  
Patient Prognosis

Background: Cytotoxic T-lymphocyte (CTL) infiltration into tumor is a positive prognostic factor in breast cancer. High tumor mutational burden (TMB) is also considered as a predictor of tumor immunogenicity and response to immunotherapy. However, it is unclear whether the infiltration of functional CTL simply reflects the TMB or represents an independent prognostic value. Methods: Utilizing The Cancer Genome Atlas (TCGA) breast cancer cohort, we established the Functional Hotness Score (FHS). The associations of FHS and breast cancer patient prognosis as well as distinct immunity markers were analyzed in a total of 3011 breast cancer patients using TCGA, METABRIC and metastatic breast cancer (MBC) cohort GSE110590. Results: We established FHS, based on CD8A, GZMB and CXCL10 gene expression levels of bulk tumors, which delivered the best prognostic value among some gene combinations. Breast cancer patients with the high-FHS tumors showed significantly better survival. FHS was lower in the MBCs. Triple-negative breast cancer (TNBC) showed the highest FHS among subtypes. FHS predicted patient survival in hormone receptor (HR)-negative, especially in TNBC, but not in HR-positive breast cancer. FHS predicted patient prognosis independently in TNBC. The high-FHS TNBCs showed not only higher CD8+ T cell infiltration, but also enhanced broader type-1 anti-cancer immunity. The patients with the high-FHS tumors showed better prognosis not only in high-TMB tumors but also in low-TMB TNBCs. The combination of high-TMB with high-FHS identified a unique subset of patients who do not recur over time in TNBC. Conclusion: TNBCs with high FHS based on the expression levels of CD8A, GZMB and CXCL10 showed improved prognosis with enhanced anti-cancer immunity regardless of TMB. FHS constitutes an independent prognostic marker of survival, particularly robustly when combined with TMB in TNBC.

Identification of CCNB2 expression in triple-negative breast cancer based on bioinformatics results

2021 ◽  
Author(s):  
jintao cao ◽  
SHUAI SUN ◽  
RAN LI ◽  
RUI MIN ◽  
XINGYU FAN ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Protein Complex ◽  
Triple Negative ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
The Core ◽  
Core Genes

Abstract Background The current epidemiology shows that the incidence of breast cancer is increasing year by year and tends to be younger. Triple-negative breast cancer is the most malignant of breast cancer subtypes. The application of bioinformatics in tumor research is becoming more and more extensive. This study provided research ideas and basis for exploring the potential targets of gene therapy for triple-negative breast cancer (TNBC). Methods We analyzed three gene expression profiles (GSE64790、GSE62931、GSE38959) selected from the Gene Expression Omnibus (GEO) database. The GEO2R online analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify the pathways and functional annotation of DEGs. Protein–protein interaction network of these DEGs were visualized by the Metascape gene-list analysis tool so that we could find the protein complex containing the core genes. Subsequently, we investigated the transcriptional data of the core genes in patients with breast cancer from the Oncomine database. Moreover, the online Kaplan–Meier plotter survival analysis tool was used to evaluate the prognostic value of core genes expression in TNBC patients. Finally, immunohistochemistry (IHC) was used to evaluated the expression level and subcellular localization of CCNB2 on TNBC tissues. Results A total of 66 DEGs were identified, including 33 up-regulated genes and 33 down-regulated genes. Among them, a potential protein complex containing five core genes was screened out. The high expression of these core genes was correlated to the poor prognosis of patients suffering breast cancer, especially the overexpression of CCNB2. CCNB2 protein positively expressed in the cytoplasm, and its expression in triple-negative breast cancer tissues was significantly higher than that in adjacent tissues. Conclusions CCNB2 may play a crucial role in the development of TNBC and has the potential as a prognostic biomarker of TNBC.

Sign in / Sign up

Export Citation Format

Share Document