Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma

Lung adenocarcinoma has entered into an era of immunotherapy with the development of immune checkpoint inhibitors (ICIs). The identification of immune subtype is crucial to prolonging survival in patients. The tumor microenvironment (TME) and metabolism have a profound impact on prognosis and therapy. The majority of previous studies focused on only one aspect, while both of them are essential to the understanding of tumorigenesis and development. We hypothesized that lung adenocarcinoma can be stratified into immune subgroups with alterations in the TME infiltration. We aimed to explore the “TME-Metabolism-Risk” patterns in each subtypes and the mechanism behind. Glycolysis and cholesterol were selected for the analysis of metabolic states based on the first half of the study. Bioinformatic analysis was performed to investigate the transcriptomic and clinical data integrated by three lung adenocarcinoma cohorts (GSE30219, GSE31210, GSE37745, N = 415). The results were validated in an independent cohort (GSE50081, N = 127). In total, 415 lung adenocarcinoma samples were integrated and analyzed. Four major immune subtypes were indentified using bioinformatic analysis. Subtype NC1, characterized by a high level of glycolysis, with extremely low microenvironment cell infiltration. Subtype NC2, characterized by the “Silence” and “Cholesterol biosynthesis Predominant” metabolic states, with a middle degree infiltration of microenvironment cell. Subtype NC3, characterized by the lack of “Cholesterol biosynthesis Predominant” metabolic state, with abundant microenvironment cell infiltration. Subtype NC4, characterized by “Mixed” metabolic state, with a relatively low microenvironment cell infiltration. Least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were performed to calculate the risk of each sample, and we attempted to find out the potential immune escape mechanism in different subtypes. The result revealed that the lack of immune cells infiltration might contribute to the immune escape in subtypes NC1 and NC4. NC3 was characterized by the high expression of immune checkpoint molecules and fibroblasts. NC2 had defects in activation of innate immune cells. There existed an obviously survival advantage in subtype NC2. Gene set enrichment analysis (GSEA) and Gene Ontology analysis indicated that the PI3K-AKT-mTOR, TGF-β, MYC-related pathways might be correlated with this phenomenon. In addition, some differentially expressed genes (DEGs) were indentified in subtype NC3, which might be potential targets for survival phenotype transformation.

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Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Journal of Oncology ◽

10.1155/2021/5512325 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ting Zhou ◽

Ping Yang ◽

Sanyuan Tang ◽

Zhongshan Zhu ◽

Xiaobing Li ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathway ◽

Risk Score ◽

Immune Cells ◽

Immune Checkpoint ◽

Software Package ◽

Immune Escape ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

R Software

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

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The Predictive Role of Immune Related Subgroup Classification in Immune Checkpoint Blockade Therapy for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.771830 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaozhou Yu ◽

Ziyang Wang ◽

Yiwen Chen ◽

Guotao Yin ◽

Jianjing Liu ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Immune Cells ◽

Immune Checkpoint ◽

Immune Checkpoint Blockade ◽

Checkpoint Blockade ◽

Gene Set Enrichment Analysis ◽

The Status ◽

Predictive Role ◽

Subgroup Classification

Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete.Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort.Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification.Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.

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Salmonella Breaks Tumor Immune Tolerance by Downregulating Tumor Programmed Death-Ligand 1 Expression

Cancers ◽

10.3390/cancers12010057 ◽

2019 ◽

Vol 12 (1) ◽

pp. 57

Author(s):

Man-Chin Chen ◽

Christian Ronquillo Pangilinan ◽

Che-Hsin Lee

Keyword(s):

T Cell ◽

Tumor Growth ◽

Tumor Cells ◽

Immune Checkpoint ◽

Immune Escape ◽

Cell Infiltration ◽

Tumor Immune Escape ◽

Programmed Death Ligand 1 ◽

T Cell Infiltration ◽

Programmed Death

Immunotherapy is becoming a popular treatment modality in combat against cancer, one of the world’s leading health problems. While tumor cells influence host immunity via expressing immune inhibitory signaling proteins, some bacteria possess immunomodulatory activities that counter the symptoms of tumors. The accumulation of Salmonella in tumor sites influences tumor protein expression, resulting in T cell infiltration. However, the molecular mechanism by which Salmonella activates T cells remains elusive. Many tumors have been reported to have high expressions of programmed death-ligand 1 (PD-L1), which is an important immune checkpoint molecule involved in tumor immune escape. In this study, Salmonella reduced the expression of PD-L1 in tumor cells. The expression levels of phospho-protein kinase B (P-AKT), phospho-mammalian targets of rapamycin (P-mTOR), and the phospho-p70 ribosomal s6 kinase (P-p70s6K) pathway were revealed to be involved in the Salmonella-mediated downregulation of PD-L1. In a tumor-T cell coculture system, Salmonella increased T cell number and reduced T cell apoptosis. Systemic administration of Salmonella reduced the expressions of PD-L-1 in tumor-bearing mice. In addition, tumor growth was significantly inhibited along with an enhanced T cell infiltration following Salmonella treatment. These findings suggest that Salmonella acts upon the immune checkpoint, primarily PD-L1, to incapacitate protumor effects and thereby inhibit tumor growth.

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Low TWEAK expression indicates poor survival and is correlated with sparse TIICs in lung adenocarcinoma (LUAD).

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e21017 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e21017-e21017

Author(s):

Jinchun Wu ◽

Xianyu Liu ◽

Yanhua Mou ◽

Shan Zeng ◽

Jin Zhang ◽

...

Keyword(s):

T Cells ◽

Lung Adenocarcinoma ◽

Cd8 T Cells ◽

Immune Cells ◽

Poor Prognosis ◽

Immune Escape ◽

Underlying Mechanism ◽

Memory Cd8 T Cells ◽

Kaplan Meier ◽

Kaplan Meier Plotter

e21017 Background: Lung adenocarcinoma (LUAD) occupies the most of non-small cell lung cancer (NSCLC) and shows promising response to PD-1 immunotherapy, but immune escape will cause treatment failure indicating poor prognosis. TWEAK (Tumor necrosis factor-related weak inducer of apoptosis, also known as TNFSF12) combining with its receptor FN14 (fibroblast growth factor–inducible 14) mediates crucial innate and adaptive immune pathways to promote the progression of multiple autoimmune diseases. So we assumed that TWEAK is a prognostic predictor and related with tumor-infiltrating immune cells (TIICs) in LUAD. Methods: TWEAK expression of LUAD was primarily investigated in The Cancer Immunome Atlas (TCIA) and then validated in Tumor Immune Estimation Resource (TIMER) databases. We assessed the effect of TWEAK on the survival via the Kaplan-Meier plotter, GEPIA2 (gene expression profiling interactive analysis) and PrognoScan databases. The relation between TWEAK and TIICs was explored in TIMER and TCIA, as well as the correlation of TWEAK and FN14 was analyzed in TIMER and GEPIA2. Results: Low TWEAK expression was significantly associated with poor relapse-free survival (RFS) (HR = 0.62, 95% CI = 0.4~0.97, logrank P = 0.035) and overall survival (OS) (HR = 0.61, 95% CI = 0.46~0.83, logrank P = 0.0012) in LUAD from Kaplan-Meier plotter. Similar impacts of TWEAK on the survival were validated in GEPIA2 and four independent cohorts from PrognoScan (jacob-00182-CANDF, GSE13213, jacob-00182-MSK and GSE31210). Moreover, reduced TWEAK expression was closely related with the paucity of TIICs which contributed to poor OS, including central memory CD8 T cells, plasmacytoid dendritic cells, activated CD8 T cells, monocytes, T follicular helper cells, immature B cells and eosinophils. In addition, TWEAK expression was positively related with the expression level of FN14 in both GEPIA2(R = 0.13, P= 0.0031) and TIMER (partial.cor = 0.212, P= 2.04e-06). Conclusions: Low TWEAK expression maybe indicate poor prognosis in LUAD, and correlated with the impaired infiltration of immune cells in the tumor region. The defective TWEAK/FN14 pathway possibly accounts for these observations, but the underlying mechanism needs to be further explored.

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SERPINE1 associated with remodeling of the tumor microenvironment in colon cancer progression: a novel therapeutic target

BMC Cancer ◽

10.1186/s12885-021-08536-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shaokun Wang ◽

Li Pang ◽

Zuolong Liu ◽

Xiangwei Meng

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Immune Cells ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

The Relationship

Abstract Background The change of immune cell infiltration essentially influences the process of colorectal cancer development. The infiltration of immune cells can be regulated by a variety of genes. Thus, modeling the immune microenvironment of colorectal cancer by analyzing the genes involved can be more conducive to the in-depth understanding of carcinogenesis and the progression thereof. Methods In this study, the number of stromal and immune cells in malignant tumor tissues were first estimated by using expression data (ESTIMATE) and cell-type identification with relative subsets of known RNA transcripts (CIBERSORT) to calculate the proportion of infiltrating immune cell and stromal components of colon cancer samples from the Cancer Genome Atlas database. Then the relationship between the TMN Classification and prognosis of malignant tumors was evaluated. Results By investigating differentially expressed genes using COX regression and protein-protein interaction network (PPI), the candidate hub gene serine protease inhibitor family E member 1 (SERPINE1) was found to be associated with immune cell infiltration. Gene Set Enrichment Analysis (GSEA) further projected the potential pathways with elevated SERPINE1 expression to carcinogenesis and immunity. CIBERSORT was subsequently utilized to investigate the relationship between the expression differences of SERPINE1 and immune cell infiltration and to identify eight immune cells associated with SERPINE1 expression. Conclusion We found that SERPINE1 plays a role in the remodeling of the colon cancer microenvironment and the infiltration of immune cells.

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B7-H5 blockade enhances CD8+ T-cell-mediated antitumor immunity in colorectal cancer

Cell Death Discovery ◽

10.1038/s41420-021-00628-4 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Jiayu Wang ◽

Hongya Wu ◽

Yanjun Chen ◽

Jinghan Zhu ◽

Linqing Sun ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

T Cell ◽

Immune Checkpoint ◽

Immune Escape ◽

Inverse Correlation ◽

Immune Checkpoint Blockade ◽

Cell Infiltration ◽

Tumor Immune Escape ◽

T Cell Infiltration

AbstractNegative immune checkpoint blockade immunotherapy has shown potential for multiple malignancies including colorectal cancer (CRC). B7-H5, a novel negative immune checkpoint regulator, is highly expressed in tumor tissues and promotes tumor immune escape. However, the clinical significance of B7-H5 expression in CRC and the role of B7-H5 in the tumor microenvironment (TME) has not been fully clarified. In this study, we observed that high B7-H5 expression in CRC tissues was significantly correlated with the lymph node involvement, AJCC stage, and survival of CRC patients. A significant inverse correlation was also observed between B7-H5 expression and CD8+ T-cell infiltration in CRC tissues. Kaplan−Meier analysis showed that patients with high B7-H5 expression and low CD8+ T-cell infiltration had the worst prognosis in our cohort of CRC patients. Remarkably, both high B7-H5 expression and low CD8+ T infiltration were risk factors for overall survival. Additionally, B7-H5 blockade using a B7-H5 monoclonal antibody (B7-H5 mAb) effectively suppressed the growth of MC38 colon cancer tumors by enhancing the infiltration and Granzyme B production of CD8+ T cells. Importantly, the depletion of CD8+ T cells obviously abolished the antitumor effect of B7-H5 blockade in the MC38 tumors. In sum, our findings suggest that B7-H5 may be a valuably prognostic marker for CRC and a potential target for CRC immunotherapy.

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Identification of an Immune-related Seven-lncRNA Signature Predicting Prognosis and Tumor-infiltrating Immune Cells in Lung Adenocarcinoma

10.21203/rs.3.rs-38155/v1 ◽

2020 ◽

Author(s):

Jinchun Wu ◽

Yanhua Mou ◽

Chunfang Zhang ◽

Chaojun Duan ◽

Bin Li

Keyword(s):

Lung Adenocarcinoma ◽

T Cell Activation ◽

Immune Cells ◽

Antigen Processing ◽

Risk Group ◽

Pearson Correlation ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Immune Pathways

Abstract Background: Lung adenocarcinoma (LUAD) is a common cancer. Immunotherapy is one of the major treatments but showing diverse efficacy in LUAD. Long non-coding RNAs (lncRNAs) are emerging as important players in immune regulation in cancer. Herein, we identified and validated a prognostic signature of immune-related lncRNAs in LUAD and explored its correlation with tumor-infiltrating immune cells (TIICs) by bioinformatics analysis.Methods: Immune-related lncRNAs were acquired using Pearson correlation analysis between lncRNAs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and immune genes from the ImmPort website and Molecular Signatures Database. The risk signature was constructed in the TCGA group through univariable Cox, lasso and multivariable Cox regression analyses. The prognostic value of the established risk signature was validated in both TCGA and GEO datasets. The interacted TIICs and immune pathways with each single lncRNA and the risk signature were investigated respectively in ImmLnc database, Cibersortx database and gene set enrichment analysis (GSEA) analyses.Results: A seven immune-related lncRNAs prognostic signature was constructed and it stratified LUAD into high and low risk groups. High risk group showed poorer overall survival (OS) in comparison with low risk group via survival analysis.The seven-lncRNAs signature was closely correlated with various TIICs and immune pathways mostly involved in T cell activation, antigen processing and presentation, chemokines and cytokine receptors.Conclusions: The seven lncRNAs model was identified as a predictable signature for prognosis of LUAD patients probably due to its immunomodulation role. This study might provide a new target for enhancing the efficacy of immunotherapy in this mortal disease.

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m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.671821 ◽

2021 ◽

Vol 9 ◽

Author(s):

Junfan Pan ◽

Zhidong Huang ◽

Yiquan Xu

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Immune Cells ◽

Prognostic Value ◽

Risk Model ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Rna Methylation

Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.

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Profiles of Immune Infiltration and Prognostic Immunoscore in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/5858092 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Yanyan Li ◽

Liping Tao ◽

Weiyang Cai

Keyword(s):

Lung Adenocarcinoma ◽

Immune Cells ◽

Cox Regression ◽

Meta Analysis ◽

Immune Therapy ◽

Interaction Network ◽

Principal Component ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Immune Microenvironment

Lung tissue is abundant with immune cells that form a powerful first defense against exotic particles and microbes. The malignant phenotype of lung adenocarcinoma (LUAD) is defined not only by intrinsic tumor cells but also by tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment. Understanding more about the immune microenvironment of LUAD could function in sorting out patients more likely with high risk and benefit from immunotherapy. Twenty-two types of TIICs were estimated based on large public LUAD cohorts from the TCGA and GEO datasets using the CIBERSORT algorithm. Then principal component analysis (PCA), meta-analysis, and single-sample gene set enrichment analysis (ssGSEA) were used to measure and evaluate the specific immune responses and relative mechanisms. Moreover, an immunoscore model based on the percent of immune cells was constructed via the univariate and multivariate Cox regression models, which provided an in-depth overview of the LUAD immune microenvironment and shed light on the immune regulatory mechanism. The differential expression genes (DEGs) were acquired based on the immunoscore model, and prognostic immune-related genes were further identified. GSEA and the protein–protein interaction network (PPI) further revealed that these genes were mostly enriched in many immune-related biological processes. It is hoped that this immune landscape could provide a more accurate understanding for LUAD development and tumor immune therapy.

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A pan-cancer analysis revealing the role of TIGIT in tumor microenvironment

Scientific Reports ◽

10.1038/s41598-021-01933-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jie Wen ◽

Xueyi Mao ◽

Quan Cheng ◽

Zhixiong Liu ◽

Fangkun Liu

Keyword(s):

T Cells ◽

Immune Cells ◽

Immune Checkpoint ◽

Expression Patterns ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction ◽

Mmr Genes ◽

Potential Biomarker ◽

Cancer Types

AbstractT cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT), an immune checkpoint, plays a pivotal role in immune suppression. However its role in tumor immunity and correlation with the genetic and epigenetic alterations remains unknown. Here, we comprehensively analyzed the expression patterns of the TIGIT and its value of prognostic prediction among 33 types of cancers based on the data collected from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression projects (GTEx). Furthermore, the correlations of TIGIT with pathological stages, tumor-infiltrating immune cells (TIICs), signatures of T cells subtypes, immune checkpoint genes, the degree of Estimation of STromal and Immune cells in MAlignant Tumor tissues using the Expression data (ESTIMATE), tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) genes, and DNA methyltransferases (DNMTs) were also explored. Gene functional enrichment was conducted by Gene Set Enrichment Analysis (GSEA). Our results showed that the expression of TIGIT was upregulated in most of the cancer types. Cox regression model showed that high expression of TIGIT in tumor samples correlates with poor prognosis in KIRC, KIRP, LGG, UVM, and with favorable prognosis in BRCA, CECS, HNSC, SKCM. TIGIT expression positively correlated with advanced stages, TIICs, the signatures of effector T cells, exhausted T cells, effector Tregs and the degree of ESTIMATE in KIRC, KIRP and UVM. TIGIT expression also positively correlated with CTLA4, PDCD1 (PD-1), CD274 (PD-L1), ICOS in most of the cancer types. Furthermore, the expression of TIGIT was correlated with TMB, MSI, MMR genes and DNMTs in different types of cancers. GSEA analysis showed that the expression of TIGIT was related to cytokine-cytokine receptor interaction, allograft rejection, oxidative phosphorylation. These findings suggested that TIGIT could serve as a potential biomarker for prognosis and a novel target for immunotherapies in cancers.

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