scholarly journals A Risk Signature Established From Three Coagulation-fibrinolysis Genes Predicts Prognosis in Digestive System Pancancer

2022 ◽  
Author(s):  
Xingyun Wang ◽  
Jinli Ji ◽  
Ying Jiang ◽  
Yiyang Zhao ◽  
Zheyao Song ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Digestive System ◽  
Risk Group ◽  
Epigenetic Modification ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Cox Analysis

Abstract Venous thromboembolism (VTE) is one of the major complications of digestive system cancer, and coagulation-fibrinolysis genes play an important role in VTE. We used univariate Cox analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analysis to construct 3-PCFGs (prognostic coagulation-fibrinolysis genes) model based on six prognostic coagulation-fibrinolysis genes. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the high- and low-risk groups. In addition, we classified digestive system pancancer patients into three clusters A, B, and C based on 3-PCFGs by K means. High-risk group and cluster C were associated with poor prognosis in digestive system pancancer. The m6A-related genes ALKBH5, FTO, RBM15, YTHDC1, and YTHDC2 (P<0.001) were highly expressed in the high-risk group and cluster C. The risk score was positively correlated with cancer-associated fibroblasts and endothelial cells. Cluster C had the highest immune score and stromal score. The poor prognosis in the high-risk group and cluster C may be affected by m6A epigenetic modification and immune microenvironment components in the digestive system pancancer.

scholarly journals A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Mei Chen ◽  
Zhenyu Nie ◽  
Yan Li ◽  
Yuanhui Gao ◽  
Xiaohong Wen ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Status ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Clinicopathological Variables

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

scholarly journals An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuo Lin ◽  
Qian Xu ◽  
Dan Miao ◽  
Fujun Yu
Keyword(s):  
Inflammatory Response ◽  
Immune Status ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Prediction ◽  
Cox Analysis

BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.MethodsIn this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.ResultsAn inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.ConclusionA novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

scholarly journals Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

2021 ◽  
Author(s):  
Jinlong Huo ◽  
Shuang Shen ◽  
Chen Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Cytolytic Activity ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

scholarly journals Targeting UCHL1 Induces Cell Cycle Arrest in High-Risk Multiple Myeloma with t(4;14)

2021 ◽  
Vol 27 ◽  
Author(s):  
Parin Kamseng ◽  
Teerapong Siriboonpiputtana ◽  
Teeraya Puavilai ◽  
Suporn Chuncharunee ◽  
Karan Paisooksantivatana ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Multiple Myeloma ◽  
High Risk ◽  
Molecular Mechanism ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Newly Diagnosed

Multiple myeloma (MM) patients considered to be at high cytogenetic risk commonly fail to respond to standard treatment. A thorough understanding of the molecular mechanism of MM development is, therefore, needed. We endeavored to explore the transcriptional signature among different subgroups of newly diagnosed MM using gene chip-based expression microarray. Bone marrow samples of 15 newly diagnosed Thai MM patients were included. The chromosomal translocation t(4;14) was the most frequently identified genetic alteration in the high-risk subgroup. Cluster analysis from expression profiling demonstrated that high-risk MM have a distinctly different expression pattern compared to standard-risk patients. The most significant differentially expressed gene was UCHL1. Functional enrichment analysis by Gene Set Enrichment Analysis, FUNRICH, and Gene Ontology Panther pathway revealed the gene sets involved in cell cycle control to be enriched in the t(4;14) high-risk group. Interestingly, among the well-established downstream targets of UCHL1, only CCND2 was significantly expressed in the t(4;14) high-risk group. Suppression of UCHL1 protein level by LDN-5744 inhibitor could arrest the cell cycle in G1 phase in cell lines. These findings shed light on the molecular mechanism of UCHL1 in t(4;14) high-risk MM and support the evidence that alteration of the UCHL1 pathway may play a role in the pathogenesis of high-risk MM.

scholarly journals Development and Validation of a Lncrnas-Based Nomogram for Survival Prediction in Neuroblastoma Patients

2021 ◽  
Author(s):  
Yong Lv ◽  
ShuGuang Jin ◽  
Bo Xiang
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment

Abstract BackgroundTreatment of neuroblastoma is evolving toward precision medicine. LncRNAs can be used as prognostic biomarkers in many types of cancer.MethodsBased on the RNA-seq data from GSE49710, we built a lncRNAs-based risk score using the least absolute shrinkage and selection operation (LASSO) regression. Cox regression, receiver operating characteristic curves were used to evaluate the association of the LASSO risk score with overall survival. Nomograms were created and then validated in an external cohort from TARGET database. Gene set enrichment analysis was performed to identify the significantly changed biological pathways. ResultsThe 16-lncRNAs-based LASSO risk score was used to separate patients into high-risk and low-risk groups. In GSE49710 cohort, the high-risk group exhibited a poorer OS than those in the low-risk group (P<0.001). Moreover, multivariate Cox regression analysis demonstrated that LASSO risk score was an independent risk factor (HR=6.201;95%CI:2.536-15.16). The similar prognostic powers of the 16-lncRNAs were also achieved in the external cohort and in stratified analysis. In addition, a nomogram was established and worked well both in the internal validation cohort (C-index=0.831) and external validation cohort (C-index=0.773). The calibration plot indicated the good clinical utility of the nomogram. Gene set enrichment analysis (GSEA) indicated that high-risk group was related with cancer recurrence, metastasis and inflammatory associated pathways.ConclusionThe lncRNA-based LASSO risk score is a promising and potential prognostic tool in predicting the survival of patients with neuroblastoma. The nomogram combined the lncRNAs and clinical parameters allows for accurate risk assessment in guiding clinical management.

scholarly journals Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi Fu ◽  
Xindong Wei ◽  
Qiuqin Han ◽  
Jiamei Le ◽  
Yujie Ma ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Response Prediction ◽  
Early Recurrence ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature

Abstract Background Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence. Methods The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database. Results Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group. Conclusions Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients.

scholarly journals Comprehensive Analysis of Tumor Immune Microenvironment and Prognosis of m6A-Related lncRNAs in Gastric Cancer

2021 ◽  
Author(s):  
Yi Wang ◽  
Gui-Qi Zhu ◽  
Di Tian ◽  
Chang-Wu Zhou ◽  
Na Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal role in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approach of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between them enrolled in the shaping of TIME remains unclear. Methods The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering was implemented to classify GC patients into 2 subtypes. Survival analysis, the infiltration of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal genes and risk score model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the discrepancies of response to immune checkpoints inhibitor (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect the expression pattern in 35 tumor tissues and paired adjacent normal tissue, and validated the prognostic value of risk model in the our cohort (N=35). Results The expression profiles of 23 lncRNAs were included to cluster patients into different subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and mutation rate of genes. Different immune cell infiltration pattern were also displayed between different clusters. GSEA showed that cluster1 was preferentially enriched with tumor hallmarks and tumor correlated biological pathways. Next, 9 lncRNAs were selected by LASSO regression model to construct risk model. Patients in the high risk group had poor prognosis. The prognosis value of this model was also validated in our cohort. As for predicting responses to the ICIs therapy, we found that patients from high risk group had lower TMB score and lower proportion of MSI-H subtype. Moreover, patients had distinct immunophenoscores in different risk groups. Conclusion Our study revealed that the potential interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to immune checkpoints inhibitors therapy.

scholarly journals Construction of a Ferroptosis-Related Nine-lncRNA Signature for Predicting Prognosis and Immune Response in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhijie Xu ◽  
Bi Peng ◽  
Qiuju Liang ◽  
Xi Chen ◽  
Yuan Cai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
High Risk ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature

Ferroptosis is an iron-dependent cell death process that plays important regulatory roles in the occurrence and development of cancers, including hepatocellular carcinoma (HCC). Moreover, the molecular events surrounding aberrantly expressed long non-coding RNAs (lncRNAs) that drive HCC initiation and progression have attracted increasing attention. However, research on ferroptosis-related lncRNA prognostic signature in patients with HCC is still lacking. In this study, the association between differentially expressed lncRNAs and ferroptosis-related genes, in 374 HCC and 50 normal hepatic samples obtained from The Cancer Genome Atlas (TCGA), was evaluated using Pearson’s test, thereby identifying 24 ferroptosis-related differentially expressed lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model were used to construct and validate a prognostic risk score model from both TCGA training dataset and GEO testing dataset (GSE40144). A nine-lncRNA-based signature (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) was identified as the ferroptosis-related prognostic model for HCC, independent of multiple clinicopathological parameters. In addition, the HCC patients were divided into high-risk and low-risk groups according to the nine-lncRNA prognostic signature. The gene set enrichment analysis enrichment analysis revealed that the lncRNA-based signature might regulate the HCC immune microenvironment by interfering with tumor necrosis factor α/nuclear factor kappa-B, interleukin 2/signal transducers and activators of transcription 5, and cytokine/cytokine receptor signaling pathways. The infiltrating immune cell subtypes, such as resting memory CD4(+) T cells, follicular helper T cells, regulatory T cells, and M0 macrophages, were all significantly different between the high-risk group and the low-risk group as indicated in Spearman’s correlation analysis. Moreover, a substantial increase in the expression of B7H3 immune checkpoint molecule was found in the high-risk group. Our findings provided a promising insight into ferroptosis-related lncRNAs in HCC and a personalized prediction tool for prognosis and immune responses in patients.

scholarly journals Comprehensive multi-omics analysis identifies an immune related predicting model in early stage breast cancer

2020 ◽  
Author(s):  
Xuan Li ◽  
Wei Jin
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Early Stage ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Early Stage Breast Cancer ◽  
Breast Cancer Patients

Abstract Background Breast cancer is known the highest incidence cancer in women. Tumor-infiltrating immune cells were reported closely related to cancers’ fate but it’s function still not very clear in breast cancer. The aim of our study is to build a infiltrating immune cells based nomogram model to better predict patients survival and explore its relationship with immune features. Methods We first use CIBERSORT to analyze 22 immune cells’ status in two unrelated breast cancer cohorts (TCGA and METRABRIC). The univariate and multivariate Cox analyses were used to analyze the prognostic ability of immune cells and other clinicopathological factors. Nomogram model were built by using independent prognostic factors. Different immune signatures and gene enrichment analysis were performed in different nomogram risk groups. Results Multivariate cox analysis showed that Macrophages M2 with HR of 1.733 (95% CI: 1.013-2.966) in TCGA cohort and 1.334 (95% CI: 1.125-1.581) in METABRIC cohort is the only independent prognostic factor among the 22 immune cells in early stage breast cancer. Macrophages M2, age, TNM stage and molecular types were used to build the nomogram model. The AUC of the ROC of nomogram reached 0.732 in TCGA cohort and 0.702 in METABRIC cohort. Using the nomogram score can better classified patients to low and high risk group. High risk group showed higher malignant signatures and predicted immunotherapy response rates than low risk group which consistent with the gene entrenchment analysis. Conclusion In this study, we revealed that M2 macrophages could predict the OS of breast cancer patients. Based on M2 and other clinical features we established a nomogram model which were significantly different in immune features that can better assess the OS risk or be a predictor for the immunotherapy response in breast cancer for further research.

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