RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

10.21203/rs.3.rs-1049643/v1 ◽

2021 ◽

Author(s):

Congli Jia ◽

Fu Yang ◽

Ruining Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

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Association of Th2 high tumors with aggressive features of breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e12584 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e12584-e12584

Author(s):

Yoshihisa Tokumaru ◽

Lan Le ◽

Masanori Oshi ◽

Eriko Katsuta ◽

Nobuhisa Matsuhashi ◽

...

Keyword(s):

Breast Cancer ◽

T Cells ◽

Cancer Progression ◽

Immune Cells ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Th2 Cells ◽

Immune Microenvironment ◽

Adaptive Immune Cells ◽

Tumor Immune Microenvironment

e12584 Background: Recent studies have shown that infiltrating T-lymphocytes have been implicated in the promotion of breast cancer progression. Upon activation, these antigen-presenting cells then recruit adaptive immune cells. It has been proposed that polarization of CD4+ effector T-cells towards the immunosuppressive Th2 cells induce cytokine release and T-cell anergy, which lead to polarization of M2 tumor-associated macrophages (TAM’s), providing a protumorigenic microenvironment. We hypothesized that there is a correlation between high levels of Th2 cells and aggressive features of breast cancer and unfavorable tumor immune environment. Methods: Clinicopathological data and overall survival information was obtained on 1069 breast cancer patients from The Cancer Genome Atlas (TCGA) database. We defined Th2 high and low levels with the median cutoff. Results: Analysis of cell composition of the immune cells within tumor immune microenvironment demonstrated that Th2 high tumors did not consistently associated with unfavorable tumor immune microenvironment. Pro-cancer immune cells, such as macrophage M2 cells were increased with Th2 high tumors whereas, regulatory T cells were decreased with Th2 high tumors (p < 0.01 and p < 0.001 respectively). On the contrary, infiltration of anti-cancer cells, such as macrophage M1 was increased whereas CD8 T cells were decreased with Th2 high tumors (p < 0.05 and p < 0.001 respectively). Th2 was not shown to have correlation with IL-4, IL-6, IL-10 and IL-13, all of which has been reported to associate with Th2 cells. Th2 levels were associated with advanced grades. Also, correlation analysis demonstrated that there was a strong correlation between Th2 levels and Ki-67. These results were further validated with gene set enrichment analysis (GSEA). GSEA revealed that in Th2 high tumors enriched the gene sets associated with cell proliferation and cell cycle. Conclusions: High expression of immunosuppressive Th2 cells was associated with highly proliferative features of breast cancer, but not with unfavorable tumor immune microenvironment.

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Bioinformatic Analysis of Immune Significance of RYR2 Mutation in Breast Cancer

BioMed Research International ◽

10.1155/2021/8072796 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zhiquan Xu ◽

Ling Xiang ◽

Rong Wang ◽

Yongfu Xiong ◽

He Zhou ◽

...

Keyword(s):

Breast Cancer ◽

Immune Response ◽

T Cells ◽

Immune Cell ◽

Cox Regression ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Specific Gene ◽

Clinical Prognosis

Background. Currently, immunotherapy is widely used for breast cancer (BC) patients, and tumor mutation burden (TMB) is regarded as a valuable independent predictor of response to immunotherapy. However, specific gene mutations and their relationship with TMB and tumor-infiltrating immune cells in BC are not fully understood. Methods. Comprehensive bioinformatic analyses were performed using data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Survival curves were analyzed via Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were used for prognosis analysis. Gene set enrichment analysis (GSEA) was performed to explore regulatory mechanisms and functions. The CIBERSORT algorithm was used to calculate the tumor-infiltrating immune cell fractions. Results. We analyzed somatic mutation data of BC from TCGA and ICGC datasets and found that 19 frequently mutated genes were reported in both cohorts, namely, SPTA1, TTN, MUC17, MAP3K1, CDH1, FAT3, SYNE1, FLG, HMCN1, RYR2 (ryanodine receptor 2), GATA3, MUC4, PIK3CA, KMT2C, TP53, PTEN, ZFHX4, MUC16, and USH2A. Among them, we observed that RYR2 mutation was significantly associated with higher TMB and better clinical prognosis. Moreover, GSEA revealed that RYR2 mutation-enriched signaling pathways were related to immune-associated pathways. Furthermore, based on the CIBERSORT algorithm, we found that RYR2 mutation enhanced the antitumor immune response by enriching CD8+ T cells, activated memory CD4+ T cells, and M1 macrophages. Conclusion. RYR2 is frequently mutated in BC, and its mutation is related to increased TMB and promotes antitumor immunity; thus, RYR2 may serve as a valuable biomarker to predict the immune response.

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A Tumor-Infiltrating Lymphocyte-Based Risk Model Predicts Prognosis of Colon Cancer

10.21203/rs.3.rs-1179877/v1 ◽

2021 ◽

Author(s):

Ziqi Sui ◽

Yanli Zhu ◽

Kejia Wu ◽

Shuxiang Wang ◽

Xixian Yuan ◽

...

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Immune Cells ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Migration And Invasion

Abstract Tumor-infiltrating lymphocytes are relevant to the tumor prognosis and response to immunotherapy in colon cancer. The gene expression data of colon cancer was obtained from the cancer genome atlas (TCGA) database and the components of immune cell types were analyzed by CIBERSORT. Selection operator (LASSO) and multivariate Cox regression filtered immune cells and selected the most significant cell types to construct an immune risk model including memory B cells, plasma cells, T follicular helper (Tfh) cells, M0 macrophages and resting dendritic cells. Receiver operating characteristic (ROC) curves were used to verify the sensitivity and specificity of the model, which was validated in Gene Expression Omnibus (GEO) dataset. Combined with the clinical traits, a nomogram was established to predict the prognosis of colon cancer. According to function analyses through weighted correlation network analysis (WGCNA) and gene set enrichment analysis (GSEA), tumor-infiltrating immune cells showed significant importance in tumor immune-associated regulation, especially the adhesion, migration and invasion in colon cancer.

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Identification of Robust Immune‐Associated lncRNAs Signature for Immune Checkpoint Blockade and Prognosis in Breast Cancer

10.21203/rs.3.rs-101307/v1 ◽

2020 ◽

Author(s):

Qianhui Xu ◽

Yuxin Wang ◽

Wen Huang

Keyword(s):

Breast Cancer ◽

T Cells ◽

Immune Checkpoint ◽

Risk Model ◽

Cox Regression ◽

Checkpoint Inhibitors ◽

Immune Checkpoint Blockade ◽

Checkpoint Blockade ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas

Abstract Background: There have numerous evidences to support that long non-coding RNAs (lncRNAs) may be crucial parts in cancer immunity. We aimed to establish a novel and robust immune-associated lncRNAs signature to improve prognostic precision in patients with breast cancer(BRCA).Methods: BRCA cases were obtained from the The Cancer Genome Atlas (TCGA) database. Immune‐related lncRNAs presenting significant association with prognosis were screened through stepwise univariate Cox regression and LASSO algorithm, and multivariate Cox regression. Kaplan-Meier analysis, ROC analyses, and proportional hazards model further conducted. The prediction reliability was further estimated in the internal validation set and combination set. Gene set enrichment analysis (GSEA) was applied for functional annotation. The correlation between immune checkpoint inhibitors and this signature was employed. Results: 13 immune-related lncRNAs were systematically identified to establish immune-related lncRNAs predictive prognosis signature. The risk model we built showed significant correlation with BRCA patients’ prognosis. The value of ROC for this lncRNAs model was up to 0.821. This immune‐related lncRNAs signature can serve as an independent prognostic biomolecular factor. Our lncRNAs signature involved in chondrocyte development, endoderm development and so forth. This lncRNAs risk model was associated with tumor immune infiltration (i.e., B cells, Dendritic, Neutrophils, CD8 T cells and CD4 T cells, etc.,) and immune checkpoint blockade (ICB) therapy key molecules (i.e., PDCD1).Conclusion: The immune‐related lncRNA signature we established possesses latent prognostic value for patients with BRCA and may have the capability to predict the clinical outcome of ICB treatment, which could provide guidance for immunological decision in patients with BRCA.

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A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.691676 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhimin Ye ◽

Shengmei Zou ◽

Zhiyuan Niu ◽

Zhijie Xu ◽

Yongbin Hu

Keyword(s):

Breast Cancer ◽

Lipid Metabolism ◽

Risk Model ◽

Cox Regression ◽

Validation Cohort ◽

Consensus Clustering ◽

Immune Microenvironment ◽

Training Cohort ◽

Cox Regression Analysis ◽

Model Based

BackgroundBreast cancer (BRCA) is the most common tumor in women, and lipid metabolism involvement has been demonstrated in its tumorigenesis and development. However, the role of lipid metabolism-associated genes (LMAGs) in the immune microenvironment and prognosis of BRCA remains unclear.MethodsA total of 1076 patients with BRCA were extracted from The Cancer Genome Atlas database and randomly assigned to the training cohort (n = 760) or validation cohort (n = 316). Kaplan–Meier analysis was used to assess differences in survival. Consensus clustering was performed to categorize the patients with BRCA into subtypes. Using multivariate Cox regression analysis, an LMAG-based prognostic risk model was constructed from the training cohort and validated using the validation cohort. The immune microenvironment was evaluated using the ESTIMATE and tumor immune estimation resource algorithms, CIBERSORT, and single sample gene set enrichment analyses.ResultsConsensus clustering classified the patients with BRCA into two subgroups with significantly different overall survival rates and immune microenvironments. Better prognosis was associated with high immune infiltration. The prognostic risk model, based on four LMAGs (MED10, PLA2G2D, CYP4F11, and GPS2), successfully stratified the patients into high- and low-risk groups in both the training and validation sets. High risk scores predicted poor prognosis and indicated low immune status. Subgroup analysis suggested that the risk model was an independent predictor of prognosis in BRCA.ConclusionThis study demonstrated, for the first time, that LMAG expression plays a crucial role in BRCA. The LMAG-based risk model successfully predicted the prognosis and indicated the immune microenvironment of patients with BRCA. Our study may provide inspiration for further research on BRCA pathomechanisms.

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Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.807410 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiang Yang ◽

Shasha Hong ◽

Xiaoyi Zhang ◽

Jingchun Liu ◽

Ying Wang ◽

...

Keyword(s):

Ovarian Cancer ◽

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Killer Cell ◽

Figo Stage ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Natural Killer Cell Cytotoxicity ◽

Tumor Immune Microenvironment

BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

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Prognostic Impact of Autophagy and Immunity Related Biomarker for Uveal Melanoma

10.21203/rs.3.rs-868531/v1 ◽

2021 ◽

Author(s):

Huibin Du ◽

Yan He ◽

Wei Lu ◽

Qi Wan

Keyword(s):

High Risk ◽

Uveal Melanoma ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Mesenchymal Transition ◽

Cox Regression Analysis

Abstract Background: Autophagy and immunity related genes serve crucial roles in carcinogenesis, but little is known about the prognostic impact for uveal melanoma (UM).Methods: Autophagy related and immunity related genes (AIRGs) expression data of 80 UM patients were obtained from the cancer genome atlas project (TCGA) database. Next, univariate cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithms were applied to build a robust AIRGs signature in TCGA and validated in another two independent datasets. Besides, UM patients classified into two subgroups based on the risk model. Differences of clinical outcome, tumor microenvironment and the likelihood of chemotherapeutic response were further explored.Results: In total, a 4-AIRGs signature was constructed and validated in various datasets, which can robustly predict patients’ metastasis-free survival (MFS) and overall survival (OS) and is an independent prognostic factor in UM. The UM patients can be classified into high and low risk subgroups by applied risk score system. The high risk group have poor clinical outcomes, higher CD8+ T cell and macrophage immune-infiltrating and more sensitive to chemotherapies. In addition, Gene Set Enrichment Analysis (GSEA) analysis revealed that hallmark epithelial-mesenchymal transition and KRAS pathways are commonly enriched in high-risk expression phenotype.Conclusion: Thus, our findings provide a new clinical strategy for the accurate diagnosis and identify a novel prognostic autophagy and immunity associated biomarker for the treatment of uveal melanoma.

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Construction of Prognostic Risk Model of 5-Methylcytosine-Related Long Non-Coding RNAs and Evaluation of the Characteristics of Tumor-Infiltrating Immune Cells in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.748279 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhidong Huang ◽

Junjing Li ◽

Jialin Chen ◽

Debo Chen

Keyword(s):

Breast Cancer ◽

Immune Cells ◽

Prognostic Value ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Non Coding Rnas

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC.Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples.Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm.Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

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Analysis of m6A-Related Signatures in the Tumor Immune Microenvironment and Identification of Clinical Prognostic Regulators in Adrenocortical Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.637933 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yi Jin ◽

Zhanwang Wang ◽

Dong He ◽

Yuxing Zhu ◽

Xueying Hu ◽

...

Keyword(s):

Adrenocortical Carcinoma ◽

Molecular Mechanisms ◽

Potential Role ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with a high rate of mortality and recurrence. N6-methyladenosine methylation (m6A) is the most common modification to affect cancer development, but to date, the potential role of m6A regulators in ACC prognosis is not well understood. In this study, we systematically analyzed 21 m6A regulators in ACC samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. We identified three m6A modification patterns with different clinical outcomes and discovered a significant relationship between diverse m6A clusters and the tumor immune microenvironment (immune cell types and ESTIMATE algorithm). Additionally, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that the m6A clusters were strongly associated with immune infiltration in the ACC. Next, to further explore the m6A prognostic signatures in ACC, we implemented Lasso (Least Absolute Shrinkage and Selection Operator) Cox regression to establish an eight-m6A-regulator prognostic model in the TCGA dataset, and the results showed that the model-based high-risk group was closely correlated with poor overall survival (OS) compared with the low-risk group. Subsequently, we validated the key modifications in the GEO datasets and found that high HNRNPA2B1 expression resulted in poor OS and event-free survival (EFS) in ACC. Moreover, to further decipher the molecular mechanisms, we constructed a competing endogenous RNA (ceRNA) network based on HNRNPA2B1, which consists of 12 long noncoding RNAs (lncRNAs) and 1 microRNA (miRNA). In conclusion, our findings indicate the potential role of m6A modification in ACC, providing novel insights into ACC prognosis and guiding effective immunotherapy.

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