scholarly journals Identification of new biomarkers and pathways associated with treatment and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Prognostic Value ◽  
Immune Cell ◽  
Gene Interaction ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Strong Positive Correlation ◽  
Hub Genes

Abstract Background Skin cutaneous melanoma (SKCM) is known as the most malignancy and treatment-resistant in human tumor, causing about 72% of deaths in skin carcinoma. However, the potential mechanism and new effective targets remain to be further elucidated. Available datasets such as Gene Expression Omnibus (GEO) can be utilized to search for novel therapeutic targets and prognostic biomarkers. Methods Three data sets were downloaded from GEO database . The differentially expressed genes (DEGs) were identified via Venn software. Protein‐protein interaction network of DEGs was developed and the module hub genes analysis was constructed by Cytoscape. Subsequently, multiple online tools and Kaplan-Meier survival curves were analyzed to detect underlying signaling pathways, gene expression, drug-gene interaction and prognostic value of hub genes. In addition, we explored the correlation between hub genes and immune cell infiltration. At last, the related miRNA, lncRNA networks were constructed by R software. Results A total of 308 DEGs and 12 hub genes were identified. Function and pathway enrichment results demonstrated a correlation between DEGs and the tumor microenvironment, immune response and melanoma tumorigenesis. Subsequently, we focused on assessing potential value of 12 hub genes. Seven hub genes ( CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 ) were identified with significant overall survival for prognosis. What’s more, five of these seven hub genes were found to be related to clinical stages (P values<0.05). In addition, the most important pathways of hub genes include interleukin-10 signaling, peptide ligand-binding receptors, which play important roles in tumor microenvironment for immune activation or immunosuppressive by regulating the infiltration of immune cells. Our results revealed a strong positive correlation between gene expression (CCL4, CCL5, CXCL9, CXCL10 and CXCL13) and immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells). Interestingly, 8 of 12 hub genes (CXCL10, CCL4, CCL5, IL6, CXCL2, PTGER3, GAL, NPY1R) were also found in the predicted drug-gene interaction. The related miRNA, lncRNA for diagnosis and prognosis were found in networks. Conclusion In conclusion, CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 were of high prognostic value and may be potential targets for the diagnosis and therapy of patients with melanoma.

scholarly journals Identification of Methylated Differentially Expressed Hub Genes and Immune Infiltration in Diabetic Retinopathy

2021 ◽  
Author(s):  
Qi Zhou ◽  
Xin Xiong ◽  
Min Tang ◽  
Yingqing Lei ◽  
Hongbin Lv
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Immune Cell ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Critical Pathways

Abstract BackgroundDiabetic retinopathy (DR), a severe complication of diabetes mellitus (DM), is a global social and economic burden. However, the pathological mechanisms mediating DR are not well-understood. This study aimed to identify differentially methylated and differentially expressed hub genes (DMGs and DEGs, respectively) and associated signaling pathways, and to evaluate immune cell infiltration involved in DR. MethodsTwo publicly available datasets were downloaded from the Gene Expression Omnibus database. Transcriptome and epigenome microarray data and multi-component weighted gene coexpression network analysis (WGCNA) were utilized to determine hub genes within DR. One dataset was utilized to screen DEGs and to further explore their potential biological functions using functional annotation analysis. A protein-protein interaction network was constructed. Gene set enrichment and variation analyses (GSVA and GSEA, respectively) were utilized to identify the potential mechanisms mediating the function of hub genes in DR. Infiltrating immune cells were evaluated in one dataset using CIBERSORT. The Connectivity Map (CMap) database was used to predict potential therapeutic agents. ResultsIn total, 673 DEGs (151 upregulated and 522 downregulated genes) were detected. Gene expression was significantly enriched in the extracellular matrix and sensory organ development, extracellular matrix organization, and glial cell differentiation pathways. Through WGCNA, one module was found to be significantly related with DR (r=0.34, P =0.002), and 979 hub genes were identified. By comparing DMGs, DEGs, and genes in WGCNA, we identified eight hub genes in DR ( AKAP13, BOC, ACSS1, ARNT2, TGFB2, LHFPL2, GFPT2, TNFRSF1A ), which were significantly enriched in critical pathways involving coagulation, angiogenesis, TGF-β, and TNF-α-NF-κB signaling via GSVA and GSEA. Immune cell infiltration analysis revealed that activated natural killer cells, M0 macrophages, resting mast cells, and CD8 + T cells may be involved in DR. ARNT2, TGFB2, LHFPL2 , and AKAP13 expression were correlated with immune cell processes, and ZG-10, JNK-9L, chromomycin-a3, and calyculin were identified as potential drugs against DR. Finally, TNFRSF1A , GFPT2 , and LHFPL2 expression levels were consistent with the bioinformatic analysis. ConclusionsOur results are informative with respect to correlations between differentially methylated and expressed hub genes and immune cell infiltration in DR, providing new insight towards DR drug development and treatment.

scholarly journals YAP1 is a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Pancreatic Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Kai Sun ◽  
Xue-de Zhang ◽  
Xiao-yang Liu ◽  
Pei Lu
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Prognostic Value ◽  
Immune Cell ◽  
Prognostic Biomarker ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Long Term Outcome

Yes-associated protein-1 (YAP1) is an important effector of the Hippo pathway and has crosstalk with other cancer signaling pathways. It induces an immunosuppressive tumor microenvironment by activating pathways in several cellular components. However, the mechanisms by which it drives immune infiltration in pancreatic cancer remain poorly understood. We analyzed the expression of YAP1 as well as its prognostic value and correlations with immune infiltrates in various cancers, with a focus on pancreatic cancer. In particular, using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA) database, we found that YAP1 is differentially expressed between tumor tissues and control tissues in a number of cancers and in particular, is elevated in pancreatic cancer. Using the Kaplan–Meier plotter, GEPIA, and Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc), we further established the prognostic value of YAP1. We found that YAP1 expression was significantly related to outcomes in multiple types of cancer based on data from The Cancer Genome Atlas, particularly in pancreatic cancer. Correlations between YAP1 and immune cell infiltration and immune cell marker expression were examined using Tumor Immune Estimation Resource and GEPIA. High expression levels of YAP1 were significantly associated with a variety of immune markers and immune cell subsets in pancreatic cancer. These results suggest that YAP1 is correlated with patient outcomes and tumor immune cell infiltration in multiple cancer types and is a valuable prognostic biomarker in pancreatic cancer.

scholarly journals The Prognostic Value of a Tumor Microenvironment-Based Immune Cell Infiltration Score Model in Colon Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingkui Tang ◽  
Minling Liu ◽  
Xijun Luo ◽  
Mengyuan Zhu ◽  
Shan Huang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Prognostic Value ◽  
Immune Cell ◽  
Wnt Signaling Pathway ◽  
Mapk Signaling ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Score Model

The current study aimed to construct a prognostic predictive model based on tumor microenvironment. CIBERSORT and ESTIMATE algorithms were used to reveal the immune cell infiltration (ICI) landscape of colon cancer. Patients were classified into three clusters by ConsensusClusterPlus algorithm. ICI scores of each patient were determined by principal component analysis. Patients were divided into high and low ICI score groups. Survival, gene expression, and somatic mutation of the two groups were compared. We found that patients with no lymph node invasion, no metastasis, T1–2 disease, and stage I–II had higher ICI scores. Calcium signaling pathway, leukocyte transendothelial migration pathway, MAPK signaling pathway, TGF β pathway, and Wnt signaling pathway were enriched in the high ICI score group. Immune-checkpoint and immune-activity associated genes were decreased in high ICI score patients. Patients in the high ICI score group had better survival. Prognostic value of ICI score was independent of tumor mutational burden (TMB). The ICI score model constructed in the current study may serve as an independent prognostic biomarker in colon cancer.

scholarly journals Comprehensive Analysis of YTH Domain Family in Lung Adenocarcinoma: Expression Profile, Association with Prognostic Value, and Immune Infiltration

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kuan Hu ◽  
Lei Yao ◽  
Yuanliang Yan ◽  
Lei Zhou ◽  
Juanni Li
Keyword(s):  
T Cells ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Immune Cell ◽  
Expression Profiles ◽  
Family Members ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Domain Family ◽  
Yth Domain

Background. All YTH domain family members are m6A reader proteins accounting for the methylation modulation involved in the process of tumorgenesis and tumor progression. However, the expression profiles and roles of the YTH domain family in lung adenocarcinoma (LUAD) remain to be further illustrated. Methods. GEPIA2 and TNMplot databases were used to generate the expression profiles of the YTH family. Kaplan-Meier plotter database was employed to analysis the prognostic value of the YTH family. Coexpression profiles and genetic alterations analysis of the YTH family were undertaken using the cBioPortal database. YTH family protein-associated protein-protein interaction (PPI) network was identified by using STRING. Functional enrichment analysis was performed with the help of the WebGestalt database. The correlation analysis between the YTH family and immune cell infiltration in LUAD was administrated by using the TIMER2.0 database. Results. mRNA expression of YTHDC1 and YTHDC2 was significantly lower in LUAD, whereas YTHDF1, YTHDF2, and YTHDF3 with apparently higher expression. YTHDF2 expression was observed to be the highest in the nonsmoker subgroup, and its expression gradually decreased with the increased severity of smoking habit. LUAD patients with low expression of YTHDC2, YTHDF1, and YTHDF2 were correlated with a better overall survival (OS) time. The YTHDF1 genetic alteration rate was 26%, which was the highest in the YTH family. The major cancer-associated functions of YTH family pointed in the direction of immunomodulation, especially antigen processing and presentation. Most of the YTH family members were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, and neutrophils, indicating the deep involvement of the YTH domain family in the immune cell infiltration in LUAD. Conclusion. The molecular and expression profiles of the YTH family were dysregulated in LUAD. YTH family members (especially YTHDC2) were promising biomarkers and potential therapeutic targets that may bring benefit for the patients with LUAD.

scholarly journals POS0736 IDENTIFICATION OF MOLECULAR PHENOTYPES AND IMMUNE CELL INFILTRATION IN SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS ACCORDING TO LONGITUDINAL GENE EXPRESSION

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 619.1-619
Author(s):  
S. Song ◽  
S. X. Zhang ◽  
J. Qiao ◽  
R. Zhao ◽  
J. Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Systemic Lupus ◽  
Signature Genes

Background:Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous clinical presentation characterized by disease unpredictable flares and multi-systemic involvement1 2. This clinical heterogeneity calls for design a molecular stratification to improve clinical trial design and formulate personalization treatment therapies.Objectives:This research was conducted to develop a reliable method to stratify SLE patients combined gene expression information and disease status.Methods:The mRNA expression profile of GSE138458 (contained 307 patients and 23 controls) and GSE49454 (contained 111 patients and 16 controls) were downloaded from the publicly GEO databases. After background adjustment, batch correction, and other pre-procession, obtaining a big gene matrix to identify the differentially expressed genes (DEGs) in SLE compared with healthy controls, which were screened by P value < 0.01. SLE subtypes were identified by non-negative matrix factorization (NMF) based on DEGs. Acquired signature genes in different SLE subtypes were conducted to process pathway enrichment analysis in Metascape. SLEDAI score and immune cell infiltration was also performed between subtypes by software package R (version 4.0.3).Results:Total 1202 DEGs were imputed to NMF unsupervised machine learning method. Patients with SLE were stratified into two subsets based on 184 signature genes derived from obtained DEGs(Fig.1A, 1B). GO and KEGG enrichment analysis showed that signature genes were mainly involved in negative regulation of innate immune response, toll-like receptor signaling pathway, regulation of immune effector process and so on(Fig.1C). Patients in Sub1 group had severe disease activity measures compared with those in Sub2(Fig.1D). SLEDAI scores from GSE49454 dataset were also higher in Sub1 compare with Sub2(Fig.1E). Further, immune cell infiltration results revealed an insufficient of regulatory T cell, CD8 T cells and naive CD4 T cells in Sub1 and neutrophils cells in Sub2(P<0.05)(Fig.1F).Conclusion:Our findings indicate that patients with SLE could be stratified into 2 subtypes which had different lymphocyte status and closely related to disease activity. This phenotyping may help us understand the etiology of the disease, inform patient in the design of clinical trials and guide treatment decision.References:[1]Dorner T, Furie R. Novel paradigms in systemic lupus erythematosus. Lancet 2019;393(10188):2344-58. doi: 10.1016/S0140-6736(19)30546-X [published Online First: 2019/06/11].[2]Fanouriakis A, Tziolos N, Bertsias G, et al. Update οn the diagnosis and management of systemic lupus erythematosus. Annals of the rheumatic diseases 2021;80(1):14-25. doi: 10.1136/annrheumdis-2020-218272 [published Online First: 2020/10/15].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Prognostic value of immune cell infiltration in bladder cancer: A gene expression‑based study

2020 ◽  
Vol 20 (2) ◽  
pp. 1677-1684 ◽  
Author(s):  
Yao Wang ◽  
Hong‑Jun Ba ◽  
Zi‑Chuan Liu ◽  
Xu‑Bin Deng ◽  
Min Zhou
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Prognostic Value ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

scholarly journals Landscape Of M6a RNA Methylation Regulators and Tumor Microenvironment Cell-Infiltration Characterization In Gastroesophageal Adenocarcinomas

2021 ◽  
Author(s):  
Duanrui Liu ◽  
Jingyu Zhu ◽  
Zongming Wang ◽  
Yusong Fang ◽  
Mingjie Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Rna Methylation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background: RNA N6-methyladenosine (m6A) modification plays a nonnegligible role in shaping individual tumor microenvironment (TME) characterizations. However, the landscape and relationship of m6A modification and TME cell infiltration remain unknown in gastroesophageal adenocarcinomas (GEA). Methods: We systematically examined the TME of GEA focusing on the distinct m6A modification patterns from the public databases. Intrinsic patterns of m6A modification were evaluated for associations with clinicopathological characteristics, underlying biological pathways, tumor immune cell infiltration, oncological outcomes and treatment responses. We generated a single-cell transcriptome atlas of the GEA sample inhouse to validate the role of the m6A modification pattern on TME.Results: We identified and validated the landscape of m6A regulators and tumor-infiltrating immune cells in GEA. Then, two distinct m6A modification patterns of GEA (cluster1/2 subgroup) were defined, and we correlated two subgroups with TME cell-infiltrating characteristics. Cluster2 subgroup correlates with a poorer prognosis, down-regulated PD-1 expression, higher risk scores and distinct immune cell infiltration. Additionally, PPI and WGCNA network analysis were integrated to identify key module genes closely related to immune infiltration of GEA to find immunotherapy markers. And COL4A1 and COL5A2 in brown module were significantly correlated to the prognosis, PD-1/L1 and CTLA-4 expression of GEA patients. Interesting, low COL5A2 expression was linked to an enhanced response to anti-PD-1 immunotherapy. Finally, a prognostic risk score was constructed using three m6A regulator-associated signatures that represented an independent prognosis factor for GEA. The single-cell transcriptome atlas of GEA sample validated the role of m6A modification pattern on TME and revealed that three m6A regulators are highly expressed in CD4+ T cells, CD8+ T cells, Tregs and Macrophages.Conclusions: Our work revealed m6A RNA methylation regulators are a type of vital participant in the malignant progression and TME diversity of GEA. m6A modification patterns of COL5A2 may be the potential biomarker contributes to predicting the response to anti-PD-1 immunotherapy.

scholarly journals Identification of Crucial Methylation Genes Associated With Immune Cell Infiltration in Twin-to-twin Transfusion Syndrome Using Co-methylation Analysis

2021 ◽  
Author(s):  
Chuang Li ◽  
Yuan Wang ◽  
Caixia Liu ◽  
Shaowei Yin
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Interaction Network ◽  
Cell Infiltration ◽  
Future Research ◽  
Regulation Mechanism ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Neurodevelopmental Abnormalities

Abstract Background: DNA methylation (DNAm), is an important transcriptional regulation mechanism, relevant to various diseases. Twin-to-twin transfusion syndrome (TTTS) is a complication in twin pregnancies resulting from disproportionate blood circulation. Survivors of TTTS show a high risk of neurodevelopmental abnormalities, particularly in the hippocampus, which is important in learning and memory. Here, we investigate gene expression and DNAm in hippocampus tissues of TTTS specimens. Methods: DNAm and gene expression levels were compared among the three groups: 10 recipients, 10 donors, and 10 matched control, using methylation microarray. We further explored the immune infiltration of six immune cell sub-populations using EpiDISH analysis. The methylated sites related to immune cell infiltration were identified using the WGCNA package. We explored the core methylation genes in the protein-protein interaction network using the MCODE plugin in Cytoscape software. Results: There were 188 differential methylation sites among three groups. Based on WGCNA, we found that the turquoise module containing 174 CpG sites is significantly related to the immune infiltration level. And four hub genes correlated with immune infiltration level, namely, PTPRJ, FYN, LYN, and AKT1, and were identified using gene sub-network analysis. Conclusions: We identify the four hub methylation genes related to immune infiltration in the TTTS. The molecular function of hub genes is still explored in the future research.

scholarly journals Identification of biomarkers and pathways associated with immune infiltration and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Primary Melanoma ◽  
Roc Curves ◽  
Cell Infiltration ◽  
Peptide Ligand ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Go Enrichment

Abstract Background Skin cutaneous melanoma (SKCM) is one of the most malignancy and aggressive cancers, causing about 72% of deaths in skin carcinoma. Although extensive study has explored the mechanism of recurrence and metastasis, the tumorigenesis of melanoma remains unclear. Exploring the tumorigenesis mechanism may help to identify prognostic biomarkers that could serve to guide cancer therapy . Methods We identified differentially expressed genes (DEGs) between patients with primary melanoma and normal skin in three data sets including GSE46517, GSE15605, GSE114445. Functional annotation including KEGG pathway and gene ontology (GO) enrichment analysis was performed by DAVID. Subsequently, protein ‐ protein interaction network of DEGs was developed and the most significant module was constructed as hub genes for further study. ClueGO was used to investigate significant pathways in hub genes. The Kaplan–Meier method was performed to assess prognostic genes. ROC curves were used to describe diagnosis value of hub genes. Then, genes expression and clinical characteristics were validated by TCGA profiles. In addition, we explored the correlation between prognostic genes and immune cell infiltration. Results A total of 308 DEGs and 12 hub genes were identified. GO enrichment results demonstrated a correlation between DEGs and the immune response, inflammatory response and transcriptional control. Hallmark pathways of hub genes including interleukin-10 signaling, chemokine receptors bind chemokine signaling ， peptide ligand-binding receptors. Survival analysis and ROC curves suggested that CCL4, CCL5, NMU, CXCL9, CXCL10, CXCL13 were independent prognostic factors (except GAL). Furthermore, prognostic genes were highly expressed in tumor tissue and related to pathological stages and Breslow depth. In addition, the expressions of CCL4, CCL5, CXCL9, CXCL10, CXCL13 were positively correlated with six immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells) and 28 types of TILs such as activated CD8 T cells, regulatory T cells, natural killer T cells while NMU and GAL were negatively correlated with it. Conclusion In summary, this study identified significant hub genes and pathways closely associated with immune infiltrations and prognosis in SKCM patients, which might help us evaluate underlying carcinogenesis progress from skin to melanoma.

scholarly journals A Comprehensive Pan-Cancer Analysis of 33 Human Cancers Reveals the Immunotherapeutic Value of Aryl Hydrocarbon Receptor

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuomao Mo ◽  
Pan Li ◽  
Zhirui Cao ◽  
Shijun Zhang
Keyword(s):  
Gene Expression ◽  
Aryl Hydrocarbon Receptor ◽  
Cancer Immunotherapy ◽  
Prognostic Value ◽  
Immune Cell ◽  
Tumor Stage ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Human Cancers ◽  
Aryl Hydrocarbon

BackgroundPrevious studies have reported the potential of aryl hydrocarbon receptor (AhR) in cancer immunotherapy. However, the mechanisms underpinning its therapeutic value have yet to be comprehensively investigated. Thus, this research aimed to explore the underlying association between AhR and cancer immunotherapy in 33 human cancers.MethodsThe gene expression data and clinical characteristics of 33 cancers were retrieved from The Cancer Genome Atlas database. The immunotherapeutic cohorts included GSE67501 and GSE78220 as well as IMvigor210, which were obtained from the Gene Expression Omnibus database and included in a previously published study respectively. Clinical parameters, including patient age, gender, survival, and tumor stage were analyzed to assess the prognostic value of AhR. The activity of AhR was generated by single sample gene set enrichment analysis and used to evaluate the difference between the AhR transcriptome and protein expression level. To better understand the role of AhR in cancer immunotherapy, the correlation between AhR and tumor microenvironment, as well as its relation to immune processes/elements, such as immune cell infiltration, immune inhibitors and stimulators, and the major histocompatibility complex were analyzed. The relevant underlying pathways associated with AhR signaling in cancer were also explored. Furthermore, the correlation between AhR and two immunotherapeutic biomarkers (tumor mutational burden and microsatellite instability) was investigated. Finally, the relationship between AhR and immunotherapeutic response was explored using three independent immunotherapeutic cohorts.ResultsAlthough AhR was not closely associated with age (5/33), gender (3/33), or tumor stage (3/21) in any of the studied human cancers, it exhibited potential prognostic value for predicting patient survival. Consistency has been observed between AhR activity and expression in some cancers (7/33). Generally, AhR presented a robust correlation with immune cell infiltration, immune modulators, and immunotherapeutic markers. Moreover, high AhR expression was significantly related to immune-relevant pathways. However, no significant correlation was observed between AhR and the immunotherapeutic response.ConclusionsThis research investigated the immunotherapeutic value of AhR in 33 human cancers, providing evidence regarding the function of AhR and its role in clinical treatment. However, considering that a bioinformatics approach was adopted, the current results are preliminary and require further validation.

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