Prognostic nomogram based on immune scores for breast cancer patients

Ju, Wang; Li, Yanling; Fu, Wenying; Zhang, Ye; Jiang, Jun; Zhang, Yi; Qi, Xiaowei

doi:10.1002/cam4.2428

Cited by 38 publications

(37 citation statements)

References 28 publications

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“…To estimate the stromal and immune infiltration level of tumor and provide clues for researches on this field, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) (4) is modeled and tested to calculate tumor purity, stromal and immune scores of patients via expression profile and give an overall view of tumor microenvironment. As indicated in recent work, high or intermediate immune score of breast cancer could bring better disease-free survival or overall survival (5). Other than confirming this discovery, we intended to investigate the pathways and genes that potentially affect tumor infiltration.…”

Section: Introductionmentioning

confidence: 90%

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

et al. 2020

Front. Med.

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Background: Tumor microenvironment is essential for breast cancer progression and metastasis. Our study sets out to examine the genes affecting stromal and immune infiltration in breast cancer progression and prognosis. Materials and Methods: This work provides an approach for quantifying stromal and immune scores by using ESTIMATE algorithm based on gene expression matrix of breast cancer patients in TCGA database. We found differentially expressed genes (DEGs) through limma R package. Functional enrichments were accessed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Besides, we constructed a protein-protein network, identified several hub genes in Cytoscape, and discovered functionally similar genes in GeneMANIA. Hub genes were validated with prognostic data by Kaplan-Meier analysis both in The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and a meta-analysis of hub genes prognosis data was utilized in multiple databases. Furthermore, their relationship with infiltrating immune cells was evaluated by Tumor IMmune Estimation Resource (TIMER) web tool. Cox regression was utilized for overall survival (OS) and recurrence-free survival (RFS) in TCGA database and OS in METABRIC database in order to evaluate the impact of stromal and immune scores on patients prognosis. Results: One thousand and eighty-five breast cancer patients were investigated and 480 differentiated expressed genes (DEGs) were found based on the analysis of mRNA expression profiles. Functional analysis of DEGs revealed their potential functions in immune response and extracellular interaction. Protein-protein interaction network gave evidence of 10 hub genes. Some of the hub genes could be used as predictive markers for patients prognosis. In this study, we found that tumor purity and specific immune cells infiltration varied in response to hub genes expression. The multivariate cox regression highlighted the fact that immune score played a detrimental role in overall survival (HR = 0.45, 95% CI: 0.27-0.74, p = 0.002) and recurrence-free survival (HR = 0.41, 95% CI: 0.22-0.77, p = 0.006) in TCGA database. These result was confirmed in Xu et al. Tumor Microenvironment in Breast Cancer METABRIC database that immune score was a protector of OS (HR = 0.88, 95% CI: 0.77-0.99, p = 0.039). Conclusions: Our findings promote a better understanding of the potential genes behind the regulation of tumor microenvironment and cells infiltration. Immune score should be considered as a prognostic factor for patients' survival.

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Section: Introductionmentioning

confidence: 90%

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

et al. 2020

Front. Med.

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“…The emergence of high-throughput nucleotide sequencing analysis provides new perspectives to understand the genomic changes in tumors, revealing the differentially expressed genetic signatures between tumor tissues and normal tissues (8,9) . Several studies in different types of cancer, including breast cancer, thyroid cancer, non-squamous non-small cell lung cancer and colorectal carcinoma, have examined differences in the patterns of immune signatures to improve our understanding of the immune environment and the mechanisms underlying tumor development and progression (10)(11)(12)(13)(14). However, the specific immune genetic changes in HCC have not been extensively studied, although one study found that the levels of immune cells infiltration, specifically by T-cells, cytotoxic cells, Th2 cells and macrophages, in HCC were associated with improved survival in patients based on n silico analysis, suggesting that the type of immune cells present in HCC tissues were different from the immune cell profile of the normal liver (15).…”

Section: Introductionmentioning

confidence: 99%

Immune system‑associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma

Huang

Chen

et al. 2020

Int J Oncol

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Hepatocellular carcinoma (HCC) is one of the most malignant types of cancer, and is associated with high recurrence rates and a poor response to chemotherapy. Immune signatures in the microenvironment of HCC have not been well explored systematically. The aim of the present study was to identify prognostic immune signatures and build a nomogram for use in clinical evaluation. Using bioinformatics analysis, RNA-seq data and overall survival (OS) information on 370 HCC cases from TCGA and 232 HCC cases from ICGC were analyzed. The differential expression of select immune genes, based on previously published studies, between HCC and adjacent tissue were analyzed using the limma package in R. Enrichment of pathways and gene ontology analysis was performed using clusterProfiler. Subsequently, univariate Cox regression analysis, Lasso penalty linear regression and multivariate Cox regression models were used to construct a model for immune risk score (IRS). The R packages, survival and survivalROC, were used to plot survival and the associated receiver operating characteristic curves. Infiltration of immune cells was calculated using Tumor IMmune Estimation Resource, with significance examined using a Pearson's correlation test. P<0.05 was considered significant. Based on the analysis, expression of 200 immune genes were upregulated and 47 immune genes were downregulated immune genes. In the multivariate Cox model, 5 genes (enhancer of zest homology 2, ferritin light chain, complement factor H related 3, isthmin 2, cyclin dependent kinase 5) were used to generate the IRS. By stratifying according to the median IRS, it was shown that patients with a high IRS had poor OS rates after 1, 2, 3 and 5 years, and this result was consistent across the testing, training and independent validation cohorts. Additionally, the IRS was correlated with the abundance of infiltrating immune cells. The nomogram built using IRS and clinical characteristics, was able to predict 1, 3 and 5 year OS with area under the curve values of >0.8. These results suggest that the model developed to calculate the IRS may be used to monitor the effectiveness of treatment strategies and for prognostic prediction.

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“…Lymphocyte-activation gene 3 (LAG3) is a transmembrane protein with structural homology to CD4 co-receptors [51]. It is found in activated CD4 T cells, regulatory T cells, Tr1 cells, activated CD8 T cells, natural killer cells, dendritic cells, B cells, and exhausted effector T cells [17,18]. LAG3 has multiple biological functions, for example, it is known to negatively regulate the proliferation, activation, and effector functions of T cells [52,53].…”

Section: Discussionmentioning

confidence: 99%

“…The ESTIMATE algorithm has been used in studies on multiple cancers, which have shown the effectiveness of such big databased algorithms [15,16]. Due to the prognostic signi cance of the integrated immune in ltration score in breast cancer [17], gastric cancer [18], oral squamous cell carcinoma [19], and ovarian cancer [20], its value can be used as an additional indicator to TNM staging system for patient survival prediction. However, very few studies have reported integrated immune in ltration in lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

Immune Score Closely Associated With Tumor Microenvironment as A Prognostic Factor in Lung Adenocarcinoma

Chen²,

Liu³

et al. 2020

Preprint

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Background: The tumor microenvironment plays a vital role in tumor biology and has recently attracted widespread attention. However, the prognostic significance of integrated immune scores in lung adenocarcinoma has not yet been identified. This study aimed to systematically estimate the association between immune scores and prognosis and develop a clinical nomogram to predict the survival of patients with lung adenocarcinoma. This study also systematically explored the underlying prognostic factors of the immune score in lung adenocarcinoma.Methods: Public datasets for lung adenocarcinoma was acquired from The Cancer Genome Atlas data portal. The immune score of each sample was calculated using the ESTIMATE algorithm. Univariate and multivariate Cox regression analyses identified several significant prognostic factors and further developed a prognostic nomogram. The C-index, calibration curve, and ROC curve were used to evaluate the predictive accuracy and discriminative ability of the resultant nomogram. Results: We found that patients with higher immune scores had a better prognosis (log rank test p = 0.0004). The nomogram that integrated the immune score could effectively stratify high-risk LUAD patients in terms of clinical response. Patients in the high-risk groups usually had a worse prognosis (log rank test p < 0.0001) and higher mortality. The mortality rate in high and low risk groups was 42.67% and 26.37%, respectively (p < 0.0001). In addition, correlation analysis showed that the immune score was significantly dependent on the mRNA expression of immunotherapy-associated biomarkers (PD-1, PD-L1, and LAG3) as well as on the presence of certain immune cell subtypes, but had no correlation with tumor mutation burden.Conclusion: The immune score is a prognostic factor in lung adenocarcinoma. The nomogram with an integrated immune score can effectively predict the survival of patients with lung adenocarcinoma. The mechanism by which the immune score estimates the prognosis of patients with lung adenocarcinoma is related to the tumor immune microenvironment.

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Prognostic nomogram based on immune scores for breast cancer patients

Cited by 38 publications

References 28 publications

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Immune system‑associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma

Immune Score Closely Associated With Tumor Microenvironment as A Prognostic Factor in Lung Adenocarcinoma

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