Comprehensive analysis of tumor mutation burden and immune microenvironment in gastric cancer

Abstract Tumor mutation burden (TMB) was a promising marker for immunotherapy. We aimed to investigate the prognostic role of TMB and its relationship with immune cells infiltration in gastric cancer (GC). We analyzed the mutation landscape of all GC cases and TMB of each GC patient was calculated and patients were divided into TMB-high and TMB-low group. Differentially expressed genes (DEGs) between the two groups were identified and pathway analysis was performed. The immune cells infiltration in each GC patient was evaluated and Kaplan–Meier analysis was performed to investigate the prognostic role of immune cells infiltration. At last, hub immune genes were identified and a TMB prognostic risk score (TMBPRS) was constructed to predict the survival outcome of GC patients. The relationships between mutants of hub immune genes and immune infiltration level in GC was investigated. We found higher TMB was correlated with better survival outcome and female patients, patients with T1-2 and N0 had higher TMB score. Altogether 816 DEGs were harvested and pathway analysis demonstrated that patients in TMB-high group were associated with neuroactive ligand–receptor interaction, cAMP signaling pathway, calcium signaling pathway. The infiltration of activated CD4+ memory T cells, follicular helper T cells, resting NK cells, M0 and M1 macrophages and neutrophils in TMB-high group were higher compared than that in TMB-low group and high macrophage infiltration was correlated with inferior survival outcome of GC patients. Lastly, the TMBPRS was constructed and GC patients with high TMBPRS had poor prognosis.

Download Full-text

Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

10.21203/rs.3.rs-20941/v1 ◽

2020 ◽

Author(s):

Lin Wang ◽

Qian Wei ◽

Ming Zhang ◽

Lianze Chen ◽

Zinan Li ◽

...

Keyword(s):

T Cells ◽

Esophageal Cancer ◽

Signaling Pathway ◽

Immune Cells ◽

Roc Curves ◽

Survival Outcome ◽

Immune Gene ◽

Prognostic Signature ◽

Immune Genes ◽

Validation Set

Abstract Background Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival in the world. The poor prognosis of ESCA is not only related to malignant cells, but also affected by the microenvironment. We aimed to establish prognostic signature consisting of immune genes to predict the survival outcome of patients and estimate the prognosis value of infiltrating immune cells in tumor microenvironment (TME). Methods Based on integrated analysis of gene expression profiling and immune gene database, differentially immune-related genes were filtered out. Then, stepwise Cox regression analysis was applied to identify survival related immune genes and construct prognosis signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognosis signature and infiltrating immune cells by Tumor Immune Estimation Resource (TIMER) and CIBERSORT. Results Finally, we identified 303 differentially immune genes as candidate and constructed immune prognosis signature composed of six immune genes. Furthermore, we observed that the prognosis signature was enriched in cytokine-mediated signaling pathway, lymphocyte activation, immune effector process, cancer pathway, NF-kappa B signaling pathway. K-M survival curves showed that the prognosis signature indeed have good predictive ability in entire ESCA set ( P =0.003), validation set 1 ( P =0.008) and validation set 2 ( P =0.036). The area under the curve (AUC) of ROC curves validated the predictive accuracy of immune signature in three cohorts (AUC=0.757, 0.800 and 0.701), respectively. In addition, we identified the prognosis value of infiltrating-immune cells including activated memory CD4 T cells, T cells follicular helper cells and monocytes and provided a landscape of TME. Conclusions The results indicated that immune prognosis signature can be a novel biomarker to predict survival outcome, which can provide new targets for immunotherapy and individualized therapies in ESCA and open up a new prospect for improving the prognosis of ESCA patients in the era of immunotherapy.

Download Full-text

MUC4 mutation correlates with tumor mutation burden and the immune microenvironment in colorectal cancer

10.21203/rs.3.rs-101882/v1 ◽

2020 ◽

Author(s):

Ting Li ◽

Wenjia Hui ◽

Halina Halike ◽

Feng Gao

Keyword(s):

Colorectal Cancer ◽

Immune Response ◽

T Cells ◽

Immune Cells ◽

Gene Mutations ◽

Cancer Genome ◽

Immune Microenvironment ◽

Tumor Mutation Burden ◽

Mutation Burden ◽

Mutant Genes

Abstract Background: Immunotherapy is a new strategy for Colorectal cancer (CRC) treatment. Tumor mutation burden (TMB) may act as an emerging biomarker for predicting responses to immunotherapy. Nevertheless, no studies investigate if these gene mutations correlate to TMB and tumor-infiltrating immune cells. Methods: Somatic mutation data for CRC samples were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Then, we investigated the relationship between these mutant genes, TMB and overall survival outcomes. GSEA analysis was performed to explore the underlying mechanism of mutant gene. Finally, we further verified the connection between gene mutations and immune response.Results: We identified 17 common mutant genes shared by both two cohorts. Further analysis found that MUC4 mutation was strongly related to higher TMB and predicted a poorer prognosis. Moreover, functional enrichment analysis of samples with MUC4 mutation revealed that they were involved in regulating the natural killer cell mediated cytotoxicity signaling pathway. Significant changes in the proportion of the immune cells of CD8 T cells, activated NK cells, M1 macrophages and resting memory CD4 T cells were observed using the CIBERSORT algorithm. Conclusions: Our research revealed that MUC4 mutation significantly correlated with increased TMB, a worse prognosis and modulating the immune microenvironment, which may be considered a biomarker to predict the outcome of the immune response in colorectal cancer.

Download Full-text

Prognostic Role of Tumor Mutation Burden Combined With Immune Infiltrates in Skin Cutaneous Melanoma Based on Multi-Omics Analysis

Frontiers in Oncology ◽

10.3389/fonc.2020.570654 ◽

2020 ◽

Vol 10 ◽

Author(s):

Junya Yan ◽

Xiaowen Wu ◽

Jiayi Yu ◽

Yanyan Zhu ◽

Shundong Cang

Keyword(s):

Cutaneous Melanoma ◽

Prognostic Role ◽

Tumor Mutation Burden ◽

Omics Analysis ◽

Mutation Burden

Download Full-text

Tumor mutation burden in connection with immune-related survival in uterine corpus endometrial carcinoma

Cancer Cell International ◽

10.1186/s12935-021-01774-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ling Zhao ◽

Xueshu Fu ◽

Xiling Han ◽

Yanjun Yu ◽

Yaping Ye ◽

...

Keyword(s):

T Cell ◽

Immune Cells ◽

Immune Cell ◽

Cd8 T Cell ◽

Prognostic Models ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Tumor Mutation Burden ◽

Immune Genes ◽

Mutation Burden

Abstract Background UCEC is the most common gynecological malignancy in many countries, and its mechanism of occurrence and development is related to tumor mutation burden (TMB) and immune cell infiltration. Therefore, it is necessary to systematically explore the TMB-related gene profile in immune cells to improve the prognosis of UCEC. Methods We integrated TMB-related genes with basic clinical information of UCEC patients based on TCGA dataset. Differentially expressed genes (DEGs) were selected through differential expression screening, PPI, and enrichment analysis. Additionally, we analyzed the components of immune cell infiltration of the DEGs to obtain the differential immunity-related genes. A single factor and multifactor Cox regression analyses were conducted to establish new prognostic indicators of OS and DFS based on TMB-related immune genes. To further study the correlation between survival and immune cell infiltration, a Cox model based on these immune infiltration compositions was built. Using the clinical variables, we established nomograms for OS and DFS. Results 393 DEGs were significantly associated with clinical outcomes and the immune component in patients with UCEC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and protein-protein interaction network (PPI) analyses revealed the role of these genes and information on related pathways. Then, two prognostic models were established based on the differential immune genes for OS (GFAP and MX2) and DFS (MX2, GFAP, IGHM, FGF20, and TRAV21). In DFS, the differential immune genes were related to CD4+ T cell, CD8+ T cell, macrophage, and neutrophil (all P < 0.05). B cell and CD8+ T cell were independent prognostic factors from among the immune cell elements in UCEC. Finally, the risk scores of these models were combined with the clinical elements-based nomogram models, and the AUC values were all over 0.7. Conclusions Our results identified several clinically significant differential immune genes and established relevant prognostic models, providing a basis for the molecular analysis of TMB and immune cells in UCEC, and identified potential prognostic and immune-related genes for UCEC. We added clinical related conditions for further analysis to confirm the identity of the genes and clinical elements-based models.

Download Full-text

Exploration of the role of tumor mutation burden in clinical significance, immunotherapy response predictor and immune cell infiltration in colon cancer

10.21203/rs.3.rs-31934/v1 ◽

2020 ◽

Author(s):

Zhengshui Xu ◽

Chao Qu ◽

Jing Guo ◽

Xiaopeng Li ◽

Yunhua Wu ◽

...

Keyword(s):

Colon Cancer ◽

T Cells ◽

Immune Cells ◽

Immune Cell ◽

Mrna Level ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Mutation Burden ◽

Pathologic Features

Abstract Backgroud:Tumor mutation burden has become a powerful bio-marker to predict prognosis and immunotherapy responsiveness to patients in various cancers, but the role of TMB in colon cancer is still unclear.Methods:The transcriptome profiling data of colon patients and the simple nucleotide variation data of colon cases were downloaded from the Cancer Genome Atlas (TCGA) database. The groups were divided into high TMB and low TMB group according to the median of TMB. Then we explored the relationship between immune checkpoints, immune cells and TMB, respectively. Results: Mutation profiles of 399 colon cancer samples were analyzed in TCGA database. The senior (age＞65) had a strong relationship with higher-TMB level(p=0.001). Low-TMB group correlated with advanced N stage (P＜0.001), M stage (P＜0.001), and pathologic stage(P＜0.001). High-TMB group had significantly higher mRNA level of PD-L1, TIGIT, HAVCR2, and LAG3 than low-TMB group, which indicated high-TMB referred to better immunotherapy responsiveness in colon cancer. And high-TMB level correlated with higher fractions of CD8T cells (p=0.021), higher CD4 memory T cells(p=0.039), follicular helper T cells (p=0.002)and M1 macrophages (p＜0.001), while the low-TMB groups correlated with higher regulator T cells (p=0.002). So high-TMB correlated with stronger immune cell infiltrationConclusions:The high TMB referred to better clinical pathologic features, better immunotherapy responsiveness and stronger immune cells infiltration in colon cancer. Hence TMB may be a very promising bio-marker to predict prognosis and immunotherapy responsiveness to patients in colon cancer.

Download Full-text

Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200407075004 ◽

2020 ◽

Vol 23 (5) ◽

pp. 381-391 ◽

Cited By ~ 1

Author(s):

Yongchun Song ◽

Yanqin Sun ◽

Tuanhe Sun ◽

Ruixiang Tang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

T Cells ◽

Tumor Microenvironment ◽

Immune Cells ◽

Molecular Subtypes ◽

Small Cell ◽

Small Cell Lung ◽

Immune Genes

Background: Tumor microenvironment (TME) cells play important roles in tumor progression. Accumulating evidence show that they can be exploited to predict the clinical outcomes and therapeutic responses of tumor. However, the role of immune genes of TME in small cell lung cancer (SCLC) is currently unknown. Objective: To determine the role of immune genes in SCLC. Methods: We downloaded the expression profile and clinical follow-up data of SCLC patients from Gene Expression Omnibus (GEO), and TME infiltration profile data of 158 patients using CIBERSORT. The correlation between TME phenotypes, genomic features, and clinicopathological features of SCLC was examined. A gene signature was constructed based on TME genes to further evaluate the relationship between molecular subtypes of SCLC with the prognosis and clinical features. Results: We identified a group of genes that are highly associated with TME. Several immune cells in TME cells were significantly correlated with SCLC prognosis (p<0.0001). These immune cells displayed diverse immune patterns. Three molecular subtypes of SCLC (TMEC1-3) were identified on the basis of enrichment of immune cell components, and these subtypes showed dissimilar prognosis profiles (p=0.03). The subtype with the best prognosis, TMEC3, was enriched with immune activation factors such as oncogene M0, oncogene M2, T cells follicular helper, and T cells CD8 (p<0.001). The TMEC1 subtype with the worst prognosis was enriched with T cells CD4 naive, B cells memory and Dendritic cells activated cells (p<0.001). Further analysis showed that the TME was significantly enriched with immune checkpoint genes, immune genes, and immune pathway genes (p<0.01). From the gene expression data, we identified four TME-related genes, GZMB, HAVCR2, PRF1 and TBX2, which were significantly associated with poor prognosis in both the training set and the validation set (p<0.05). These genes may serve as markers for monitoring tumor responses to immune checkpoint inhibitors. Conclusion: This study shows that TME features may serve as markers for evaluating response of SCLC cells to immunotherapy.

Download Full-text

Tumor Mutation Burden: A Predictive Biomarker for Gastric Cancer Immunotherapy

10.21203/rs.3.rs-74769/v1 ◽

2020 ◽

Author(s):

Renshen Xiang ◽

Tao Fu

Keyword(s):

Gastric Cancer ◽

T Cells ◽

Clinical Decision Making ◽

Immune Cell ◽

Predictive Biomarker ◽

Clinical Decision ◽

Tumor Mutation Burden ◽

Follicular Helper ◽

Tumor Purity ◽

Mutation Burden

Abstract Background: Few studies have focused on the underlying relationship between the prognosis of tumor mutation burden (TMB) and immune cell infiltration in gastric cancer (GC). This study aims to explore the relationship among TMB and various components in tumor microenvironment (TME). Methods: The transcription profiles and somatic mutation data of 375 tumor and 32 normal samples were obtained from TCGA. The specific mutation information was summarized and visualized with waterfall chart, then number of TMB per million bases of each GC sample was calculated. Immune/stromal scores and tumor purity were calculated by the ‘ESTIMATE’ package, and the fractions of 22 immune cells in each sample were evaluated by CIBERSORT algorithm. Finally, Lass regression analysis was utilized to generate a prognostic scoring signature with TCGA cohort as the training set, while GES84437 cohort as the validation set. Results: Higher TMB indicated favorable overall survival (OS, P = 0.043)，better disease specific survival (P = 0.029), and longer progression free interval (P = 0.004). TMB was positively correlated with MSI and tumor purity, while negatively associated with immune/stromal scores. Moreover, TMBhigh group has lower T cells CD4 memory resting (P < 0.001) and T cells regulatory (P < 0.001), and more T cells CD4 memory activated (P < 0.001) and T cells follicular helper (P = 0.009). More importanly, the infiltration of dendritic cells activated predicted a worse OS, while T cells CD4 memory activated and T cells follicular helper meant a better OS. Finally, a nomogram combined TMB-related signature with clinicopathologic variables can successfully predict the OS with high accuracy and efficiency.Conclusion: TMB can effectively reveal the immune infiltration status in TME of GC, and might serve as a prognostic classifier for individualized treatment of clinical decision-making.

Download Full-text

Identification and Comprehensive Prognostic Analysis of a Novel Chemokine-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.797341 ◽

2022 ◽

Vol 9 ◽

Author(s):

Xiaolong Liang ◽

Gangfeng Yu ◽

Lang Zha ◽

Xiong Guo ◽

Anqi Cheng ◽

...

Keyword(s):

Gastric Cancer ◽

Immune Cells ◽

Risk Model ◽

Poor Survival ◽

Tumor Mutation Burden ◽

Prognostic Performance ◽

Prognostic Analysis ◽

Mutation Burden ◽

Genes Expression ◽

Checkpoint Genes

Gastric cancer (GC) is a malignant tumor with poor survival outcomes. Immunotherapy can improve the prognosis of many cancers, including GC. However, in clinical practice, not all cancer patients are sensitive to immunotherapy. Therefore, it is essential to identify effective biomarkers for predicting the prognosis and immunotherapy sensitivity of GC. In recent years, chemokines have been widely reported to regulate the tumor microenvironment, especially the immune landscape. However, whether chemokine-related lncRNAs are associated with the prognosis and immune landscape of GC remains unclear. In this study, we first constructed a novel chemokine-related lncRNA risk model to predict the prognosis and immune landscape of GC patients. By using various algorithms, we identified 10 chemokine-related lncRNAs to construct the risk model. Then, we determined the prognostic efficiency and accuracy of the risk model. The effectiveness and accuracy of the risk model were further validated in the testing set and the entire set. In addition, our risk model exerted a crucial role in predicting the infiltration of immune cells, immune checkpoint genes expression, immunotherapy scores and tumor mutation burden of GC patients. In conclusion, our risk model has preferable prognostic performance and may provide crucial clues to formulate immunotherapy strategies for GC.

Download Full-text