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

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

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

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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Constructing Tumor Immune Microenvironment and Identifying the Immune-Related Prognostic Signatures in Colorectal Cancer Using Multi-omics Data

10.1101/2021.08.30.21262762 ◽

2021 ◽

Author(s):

Shuai Zhang ◽

Jiali Lv ◽

Bingbing Fan ◽

Zhe Fan ◽

Chunxia Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Cross Validation ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Omics Data ◽

Epigenetic Alterations ◽

Lasso Regression ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

ABSTRACTBackgroundThe tumor immune microenvironment (TIME) plays a key role in occurrence, progression and prognosis of colorectal cancer (CRC). However, the genetic and epigenetic alterations and potential mechanisms in the TIME of CRC are still unclear.MethodsWe investigated the immune-related differences in three types of genetic or epigenetic alterations (gene expression, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and the immune-related biological processes by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step method based on LASSO regression and Cox regression was used to construct the prognostic prediction model. Cross validation was performed to validate the model.ResultsA total of 1,745 differentially expressed genes, 178 differentially mutated genes and 1,961 differentially methylation probes were identified between the high-immunity group and the low-immunity group. We retained 15 genetic and epigenetic variables after using LASSO regression and Cox regression. For the prognostic predictions on the TCGA profiles, the performance of the model with 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.861, 0.797, and 0.875. Finally, the overall risk score model was constructed based on genetic, epigenetic, demographic and clinical characteristics, which comprised 18 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.865), 3-year (AUC = 0.839), and 5-year (AUC = 0.914) survival predictions. Kaplan-Meier survival analysis demonstrated that the overall survival of the high-risk group was significantly poorer compared with the low-risk group. Prognostic nomogram, calibration plot and cross validation showed excellent predictive performance.ConclusionsOur study provides a new clue to explore the TIME of CRC patients in genetic and epigenetic aspects. Meanwhile, the prognostic model also has clinical prognostic value and may provide new indicators for the treatment of CRC patients.

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Development and Validation of a Novel Ferroptosis-Related Gene Signature for Predicting Prognosis and the Immune Microenvironment in Gastric Cancer

BioMed Research International ◽

10.1155/2021/6014202 ◽

2021 ◽

Vol 2021 ◽

pp. 1-21

Author(s):

Feng Wang ◽

Cheng Chen ◽

Wei-Peng Chen ◽

Zu-Ling Li ◽

Hui Cheng

Keyword(s):

Gastric Cancer ◽

Cox Regression ◽

Risk Group ◽

Checkpoint Inhibitors ◽

Prognostic Index ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Blue Module

Ferroptosis is a mode of regulated cell death that depends on iron and plays pivotal roles in regulating various biological processes in human cancers. However, the role of ferroptosis in gastric cancer (GC) remains unclear. In our study, a total of 2721 differentially expressed genes (DEGs) were filtered based on The Cancer Genome Atlas (TCGA) ( n = 375 ) dataset. Weighted gene coexpression network (WGCNA) analysis was then used and identified 7 modules, of which the blue module with the most significant enrichment result was selected. By taking the intersections of the blue module and ferroptosis-related genes (FRGs), we obtained 23 common genes. Functional analysis was performed to explore the biological function of the genes of interest, and with univariate Cox regression (UCR) analysis, survival genes were screened to construct a prognostic model based on 3 genes (SLC1A5, ANGPTL4, and CGAS), which could play a role in predicting the survival of GC patients. UCR and multivariate Cox regression (MCR) analysis revealed that the prognostic index could be used as an independent prognostic indicator and validated using another GSE84437 dataset. Notably, patients in the high-risk group had higher mutation frequencies, such as TTN and TP53. TIMER analysis demonstrated that the risk score strongly correlated with macrophage and CD4+ T cell infiltration. In addition, the high- and low-risk groups illustrated different distributions of different immune statuses. Furthermore, the low-risk group had a higher immunophenoscore (IPS), which meant a better response to immune checkpoint inhibitors (ICIs). Finally, gene set enrichment analysis (GSEA) revealed several significant pathways involved in GC. In this study, a novel FRG signature was built that could predict GC prognosis and reflect the status of the tumor immune microenvironment.

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N6-Methylandenosine-Related lncRNA Signature Is a Novel Biomarkers of Prognosis and Immune Response in Colon Adenocarcinoma Patients

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.703629 ◽

2021 ◽

Vol 9 ◽

Author(s):

Peiling Zhang ◽

Guolong Liu ◽

Lin Lu

Keyword(s):

Clustering Analysis ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Colon Adenocarcinoma ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Data Set ◽

Survival Status ◽

Tumor Immune Microenvironment

BackgroundColon adenocarcinoma (COAD) is the most common type of colon cancer. To date, however, the prognostic values of m6A RNA methylation-related long non-coding RNAs (lncRNAs) in COAD are largely unknown.Materials and MethodsThe m6A-related lncRNAs were identified from The Cancer Genome Atlas (TCGA) data set. Univariate and multivariate Cox regression analyses were performed to explore the prognostic m6A-related lncRNAs. Consistent clustering analysis was performed to classify the COAD patients into different subgroups based on the expression of m6A-related lncRNAs. The potential biological functions as well as differences in the stemness index and tumor immune microenvironment between different subgroups were analyzed. The prognostic m6A-related lncRNAs were used to establish an m6A-related lncRNA risk model to predict prognosis and survival status.ResultsWe identified 31 m6A-associated lncRNAs with prognostic values from the TCGA data set. Based on the expression of prognostic m6A-associated lncRNAs, TCGA-COAD patients were classified into three clusters using consistent clustering analysis. There was a low correlation of tumor stemness between the three clusters but a significant correlation with the tumor immune microenvironment as well as the tumor mutational load. Thirty-one prognostic-related m6A-associated lncRNAs were used to construct a risk model, which was further determined by survival analysis, receiver operating characteristic (ROC) curve, and univariate and multifactor Cox analysis. The m6A-related risk model demonstrates good performance in predicting prognosis and survival status. The model-based high-risk group exhibited poorer overall survival (OS) compared with the low-risk group.ConclusionIn this study, we construct a risk model that consists of 31 m6A-related lncRNAs with independent prognostic values in COAD. Our study shows the critical roles of these 31 m6A-related lncRNAs in the tumor immune microenvironment, indicating the prospect of informing prognostic stratification and the development of immunotherapeutic strategies for COAD patients.

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Prognostic Modeling of Patients with Metastatic Melanoma Based on Tumor Immune Microenvironment Characteristics

10.21203/rs.3.rs-730205/v1 ◽

2021 ◽

Author(s):

Jing Liu ◽

Ting Ye ◽

Xue fang Zhang ◽

Yong jian Dong ◽

Wen feng Zhang ◽

...

Keyword(s):

High Risk ◽

Metastatic Melanoma ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Risk Scores ◽

Immune Microenvironment ◽

Immune Infiltration ◽

Tumor Immune Microenvironment

Abstract Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs.Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes (DEGs) were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic (ROC) curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT, Xcell and ssGSEA in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 were significantly different in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes (ALOX5AP, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) from the nine-IRG prognostic model, of which the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, we analyzed the prognostic ability and expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 in metastatic melanoma. Overall, a prognostic model for metastatic melanoma based on the characteristics of the tumor immune microenvironment was established, which was helpful for further studies.It could function well in helping people to understand the characteristics of the immune microenvironment in metastatic melanoma and to find possible therapeutic targets.

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Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.611626 ◽

2021 ◽

Vol 7 ◽

Author(s):

Xiaoyu Deng ◽

Qinghua Bi ◽

Shihan Chen ◽

Xianhua Chen ◽

Shuhui Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Prediction Ability ◽

Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

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Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.715764 ◽

2021 ◽

Vol 8 ◽

Author(s):

Li Zhang ◽

Xianzhe Tang ◽

Jia Wan ◽

Xianghong Zhang ◽

Tao Zheng ◽

...

Keyword(s):

High Risk ◽

Soft Tissue ◽

Cox Regression ◽

Soft Tissue Sarcomas ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Consensus Clustering ◽

Immune Microenvironment

Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations.Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature.Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS.Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

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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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A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

Journal of Oncology ◽

10.1155/2021/2298973 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xi Chen ◽

Lijun Yan ◽

Yu Lu ◽

Feng Jiang ◽

Ni Zeng ◽

...

Keyword(s):

Regression Analysis ◽

High Risk ◽

Adrenocortical Carcinoma ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Immune Microenvironment ◽

Gene Sets ◽

Tumor Immune Microenvironment ◽

Hypoxia Signature

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

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