PD-L1 and Immune Infiltration of m6A RNA Methylation Regulators and Its miRNA Regulators in Hepatocellular Carcinoma

Background. The aim of this study was to systematically evaluate the relationship between the expression of m6A RNA methylation regulators and prognosis in HCC. Methods. We compared the expression of m6A methylation modulators and PD-L1 between HCC and normal in TCGA database. HCC samples were divided into two subtypes by consensus clustering of data from m6A RNA methylation regulators. The differences in PD-L1, immune infiltration, and prognosis between the two subtypes were further compared. The LASSO regression was used to build a risk score for m6A modulators. In addition, we identified miRNAs that regulate m6A regulators. Results. We found that fourteen m6A regulatory genes were significantly differentially expressed between HCC and normal. HCC samples were divided into two clusters. Of these, there are higher PD-L1 expression and poorer overall survival (OS) in cluster 1. There was a significant difference in immune cell infiltration between cluster 1 and cluster 2. Through the LASSO model, we obtained 12 m6A methylation regulators to construct a prognostic risk score. Compared with patients with a high-risk score, patients with a low-risk score had upregulated PD-L1 expression and worse prognosis. There was a significant correlation between risk score and tumor-infiltrating immune cells. Finally, we found that miR-142 may be the important regulator for m6A RNA methylation in HCC. Conclusion. Our results suggest that m6A RNA methylation modulators may affect the prognosis through PD-L1 and immune cell infiltration in HCC patients. In addition, the two clusters may be beneficial for prognostic stratification and improving immunotherapeutic efficacy.

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Identification of an m6A Regulators-Mediated Prognosis Signature For Survival Prediction and Its Relevance to Immune Infiltration in Melanoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.718912 ◽

2021 ◽

Vol 9 ◽

Author(s):

Liuxing Wu ◽

Xin Hu ◽

Hongji Dai ◽

Kexin Chen ◽

Ben Liu

Keyword(s):

High Risk ◽

Immune Cell ◽

Risk Group ◽

Low Risk ◽

Cell Infiltration ◽

Survival Prediction ◽

Immune Cell Infiltration ◽

Immune Infiltration ◽

Related Risk ◽

Significant Difference

Despite robust evidence for the role of m6A in cancer development and progression, its association with immune infiltration and survival outcomes in melanoma remains obscure. Here, we aimed to develop an m6A-related risk signature to improve prognostic and immunotherapy responder prediction performance in the context of melanoma. We comprehensively analyzed the m6A cluster and immune infiltration phenotypes of public datasets. The TCGA (n = 457) and eleven independent melanoma cohorts (n = 758) were used as the training and validation datasets, respectively. We identified two m6A clusters (m6A-clusterA and m6A-clusterB) based on the expression pattern of m6A regulators via unsupervised consensus clustering. IGF2BP1 (7.49%), KIAA1429 (7.06%), and YTHDC1 (4.28%) were the three most frequently mutated genes. There was a correlation between driver genes mutation statuses and the expression of m6A regulators. A significant difference in tumor-associated immune infiltration between two m6A clusters was detected. Compared with m6A-clusterA, the m6A-clusterB was characterized by a lower immune score and immune cell infiltration but higher mRNA expression-based stemness index (mRNAsi). An m6A-related risk signature consisting of 12 genes was determined via Cox regression analysis and divided the patients into low- and high-risk groups (IL6ST, MBNL1, NXT2, EIF2A, CSGALNACT1, C11orf58, CD14, SPI1, NCCRP1, BOK, CD74, PAEP). A nomogram was developed for the prediction of the survival rate. Compared with the high-risk group, the low-risk group was characterized by high expression of immune checkpoints and immunophenoscore (IPS), activation of immune-related pathways, and more enriched in immune cell infiltrations. The low-risk group had a favorable prognosis and contained the potential beneficiaries of the immune checkpoint blockade therapy and verified by the IMvigor210 cohort (n = 298). The m6A-related signature we have determined in melanoma highlights the relationships between m6A regulators and immune cell infiltration. The established risk signature was identified as a promising clinical biomarker of melanoma.

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Comprehensive Analysis of PD-L1 Expression, Immune Infiltrates, and m6A RNA Methylation Regulators in Esophageal Squamous Cell Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.669750 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wei Guo ◽

Fengwei Tan ◽

Qilin Huai ◽

Zhen Wang ◽

Fei Shao ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

T Cells ◽

Esophageal Squamous Cell Carcinoma ◽

Immune Cell ◽

Immune Cell Infiltration ◽

Consensus Clustering ◽

Prognostic Signature ◽

Rna Methylation ◽

M6a Rna Methylation ◽

Immune Score

BackgroundEsophageal squamous cell carcinoma (ESCC) is one of the most common cancer types and represents a threat to global public health. N6-Methyladenosine (m6A) methylation plays a key role in the occurrence and development of many tumors, but there are still few studies investigating ESCC. This study attempts to construct a prognostic signature of ESCC based on m6A RNA methylation regulators and to explore the potential association of these regulators with the tumor immune microenvironment (TIME).MethodsThe transcriptome sequencing data and clinical information of 20 m6A RNA methylation regulators in 453 patients with ESCC (The Cancer Genome Atlas [TCGA] cohort, n = 95; Gene Expression Omnibus [GEO] cohort, n = 358) were obtained. The differing expression levels of m6A regulators between ESCC and normal tissue were evaluated. Based on the expression of these regulators, consensus clustering was performed to investigate different ESCC clusters. PD-L1 expression, immune score, immune cell infiltration and potential mechanisms among different clusters were examined. LASSO Cox regression analysis was utilized to obtain a prognostic signature based on m6A RNA methylation modulators. The relationship between the risk score based on the prognostic signature and the TIME of ESCC patients was studied in detail.ResultsSix m6A regulators (METTL3, WTAP, IGF2BP3, YTHDF1, HNRNPA2B1 and HNRNPC) were observed to be significantly highly expressed in ESCC tissues. Two molecular subtypes (clusters 1/2) were determined by consensus clustering of 20 m6A modulators. The expression level of PD-L1 in ESCC tissues increased significantly and was significantly negatively correlated with the expression levels of YTHDF2, METL14 and KIAA1429. The immune score, CD8 T cells, resting mast cells, and regulatory T cells (Tregs) in cluster 2 were significantly increased. Gene set enrichment analysis (GSEA) shows that this cluster involves multiple hallmark pathways. We constructed a five-gene prognostic signature based on m6A RNA methylation, and the risk score based on the prognostic signature was determined to be an independent prognostic indicator of ESCC. More importantly, the prognostic value of the prognostic signature was verified using another independent cohort. m6A regulators are related to TIME, and their copy-number alterations will dynamically affect the number of tumor-infiltrating immune cells.ConclusionOur study established a strong prognostic signature based on m6A RNA methylation regulators; this signature was able to accurately predict the prognosis of ESCC patients. The m6A methylation regulator may be a key mediator of PD-L1 expression and immune cell infiltration and may strongly affect the TIME of ESCC.

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The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.739594 ◽

2021 ◽

Vol 9 ◽

Author(s):

Sitong Zhou ◽

Yidan Sun ◽

Tianqi Chen ◽

Jingru Wang ◽

Jia He ◽

...

Keyword(s):

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Gene Signature ◽

Clinicopathological Characteristics ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Immune Infiltration ◽

Cluster A

The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored.Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p < 0.001) and metastatic SKCM (p < 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens.Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

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Immuno-Genomic Landscape of Cutaneous Melanoma Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature

10.21203/rs.3.rs-617208/v1 ◽

2021 ◽

Author(s):

Ronghua Yang ◽

Yidan Sun ◽

Tianqi Chen ◽

Jiehua Li ◽

Xiaobing Pi ◽

...

Keyword(s):

Risk Score ◽

Cutaneous Melanoma ◽

Immune Cell ◽

Gene Signature ◽

Low Risk ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Gene Set ◽

Immune Infiltration

Abstract BackgroundThe tumorigenesis of Skin cutaneous melanoma (SKCM) is still a mystery. Our study conducted a comprehensive analysis of the immune cell infiltration in the TME of SKCM. Based on the differential expression genes in the cluster grouped by the immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration were explored.MethodsWe analyzed the immune cell infiltration in two independent cohorts, and then assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways and gene mutations. We further divided the three clusters of differential genes into two groups with different unique biological processes. The Signature gene-A gene set was mainly manifested as exon skipping (ES) in SKCM patients, while the Signature gene-B gene set has no obvious alternative splicing form. Subsequently, we not only analyzed the genetic variation of the two signatures, but also constructed a ceRNA regulatory network..LASSO Cox regression was utilized to find the immune infiltration signature and the risk score of SKCM. ResultWe finally obtained 13 Hub genes, and calculated the risk score based on the coefficient of each gene to further explore the impact of the high and low-risk score on the biologically related functions and prognosis of SKCM patients.The correlation between the risk score and the clinicopathological characteristics of SKCM patients indicated that the low risk score was associated with TMECluster-A classification (P <0.001) and metastatic SKCM (P <0.001). We finally obtained 13 Hub genes which showed different prognostic effects in pan-cancers. The IHC staining results showed that Ube2L6, SRPX2, IFIT2 were higher expression while CLEC4E, END3, KIR2DL4 were lower expression in 25 melanoma specimens.ConclusionWe performed a comprehensive assessment of SKCM's immune environment and constructed a set of unprecedented immune signatures related to the immune landscape (EDN3、CLEC4E、SRPX2、KIR2DL4、UBE2L6、IFIT2), which are correlated with the different prognosis and drug response of SKCM. The immune gene signature we constructed can be used as a robust prognostic biomarker of SKCM and a predictor of immunotherapy effect.

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NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer

BioMed Research International ◽

10.1155/2022/5092505 ◽

2022 ◽

Vol 2022 ◽

pp. 1-30

Author(s):

Qiuxiang Chen ◽

Xiaojing Du ◽

Sunkuan Hu ◽

Qingke Huang

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Cancer Metabolism ◽

Gene Signature ◽

Sufficient Evidence ◽

Consensus Clustering ◽

Lasso Regression ◽

Immune Infiltration ◽

Related Risk ◽

Cluster 2

Background. Sufficient evidence indicated the crucial role of NF-κB family played in gastric cancer (GC). The novel discovery that NF-κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF-κB, metabolism, and cancer immunity in GC still requires further improvement. Methods. TCGA, hTFtarget, and MSigDB databases were employed to identify NF-κB-related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients’ response to immunotherapy. We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets. Results. We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC. Conclusion. Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF-κB-mediated cancer metabolism and immunity.

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Distinct genomic and immune microenvironment between thymomas and thymic carcinomas.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e13529 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e13529-e13529

Author(s):

Kaicheng Wang ◽

Suxia Lin ◽

Xue Hou ◽

Yongdong Liu ◽

Meichen Li ◽

...

Keyword(s):

Immune Cell ◽

Immune Escape ◽

Nk Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Tissue Samples ◽

Thymic Epithelial Tumors ◽

Immune Infiltration ◽

Thymic Carcinomas

e13529 Background: Thymomas and thymic carcinomas which uniformly known as thymic epithelial tumors (TETs) are rare intrathoracic malignancies and a limited studies have been reported addressing the molecular biology and immune discrepancy. The main purpose of this study was to depict the genomic and transcriptomic landscape of thymomas and thymic carcinomas, as well as elucidate the differentiated immune microenvironment. Methods: Totally 15 thymomas and 7 thymic carcinomas patients were enrolled from January 2014 to July 2018. Treatment-naïve tissue samples were collected, and we also obtained matched peripheral blood mononucleocytes as negative control. DNA and RNA were co-extracted and performed with whole exon and transcriptome sequencing. The immune cell infiltration scores were estimated using ssGSEA algorithm. Results: Exome sequencing revealed that GTF2I mutation occurred in all of type A thymomas but was absent in the aggressive subtypes. The median tumor mutation burden of thymomas was 0.12/Mb, significantly lower than thymic carcinomas (median: 1.02/Mb, p = 0.001). Copy number variation was more common in thymic carcinomas than thymomas (83.3% vs 9.1%, p = 0.005). Top mutational signatures enriched in both thymomas and thymic carcinomas included age and Aristolochic acid exposure, while the APOBEC signature was more common in thymomas than thymic carcinomas (81.8% vs 16.7%, p = 0.03). As a confirmed immune escape event, loss of heterozygosity of human leukocyte antigen was identified in 9.1% of thymomas and 50% of thymic carcinomas. Via unsupervised clustering of immune infiltration, all tissue samples were classified into high- and low-infiltration subgroups. Remarkably, up to 71.4% of samples from thymic carcinomas and only 6.7% of samples from thymomas were defined as low immune cell infiltration. In consideration of specific immune cell types, macrophage ( p = 0.01) and neutrophil ( p = 0.02) were enriched in thymic carcinomas while CD56+ NK cell ( p = 0.005) was enriched in thymomas, indicating the evidential discrepancy about immune cell infiltration between two subtypes of TETs. Conclusions: This study elucidated the molecular and immune microenvironment discrepancy between two subtypes of TETs. From molecular perspective, thymomas and thymic carcinomas are entirely different diseases with different etiology and characterized by distinct immune infiltration, and thus should be managed with disparate therapeutic strategies. Findings in this study may also be useful in future targets development and exploration of immunotherapies in TETs.

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A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.637743 ◽

2021 ◽

Vol 8 ◽

Author(s):

Mingqin Ge ◽

Jie Niu ◽

Ping Hu ◽

Aihua Tong ◽

Yan Dai ◽

...

Keyword(s):

Thyroid Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Low Risk ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

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Follistatin-like 1 (FSTL1) is a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Gastric Cancer

10.21203/rs.3.rs-60171/v1 ◽

2020 ◽

Author(s):

Li Li ◽

Shanshan Huang ◽

Yangyang Yao ◽

Jun Chen ◽

Junhe Li ◽

...

Keyword(s):

Gastric Cancer ◽

Tumor Immunity ◽

Survival Data ◽

Immune Cell ◽

Bioinformatic Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Western Blot Assay ◽

Immune Infiltration ◽

Normal Tissues

Abstract Background: Follistatin-like 1 (FSTL1) plays a central role in the progression of tumor and tumor immunity. However, the effect of FSTL1 on the prognosis and immune infiltration of gastric cancer (GC) remains to be elucidated.Method: The expression of FSTL1 data was analyzed in Oncomine and TIMER databases. Analyses of clinical parameters and survival data were conducted by Kaplan-Meier plotter and immunohistochemistry. Western blot assay and real‐time quantitative PCR (RT-qPCR) was using to analyzed protein and mRNA expression, respectively. The correlations between FSTL1 and cancer immune infiltrates was analyzed by Tumor Immune Estimation Resource (TIME), Gene Expression Profiling Interactive Analysis (GEPIA) and LinkedOmics database.Results: The expression of FSTL1 was significantly higher in GC tissues than in normal tissues, and bioinformatic analysis and Immunohistochemistry (IHC) indicated that high FSTL1 expression significantly correlated with poor prognosis in GC. Moreover, FSTL1 was predicted as an independent prognostic factor in GC patients. Bioinformatics analysis results suggested that FSTL1 mainly involved in tumor progression and tumor immunity. And significant correlations were found between FSTL1 expression and immune cell infiltration in GC.Conclusion: The study effectively revealed useful information about FSTL1 expression, prognostic values, potential functional networks and impact of tumor immune infiltration in GC. In summary, FSTL1 can be used as a biomarker for prognosis and evaluating immune cell infiltration in GC.

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RNA N6-Methyladenosine Regulator-Mediated Methylation Modifications Pattern and Immune Infiltration Features in Glioblastoma

Frontiers in Oncology ◽

10.3389/fonc.2021.632934 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yimin Pan ◽

Kai Xiao ◽

Yue Li ◽

Yuzhe Li ◽

Qing Liu

Keyword(s):

Immune Cell ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Lasso Regression ◽

Gene Set Enrichment ◽

Clinical Prognosis ◽

Prognostic Assessment ◽

The Relationship

Glioblastoma (GBM) is a group of intracranial neoplasms with intra-tumoral heterogeneity. RNA N6-methyladenosine (m6A) methylation modification reportedly plays roles in immune response. The relationship between the m6A modification pattern and immune cell infiltration in GBM remains unknown. Utilizing expression data of GBM patients, we thoroughly explored the potential m6A modification pattern and m6A-related signatures based on 21 regulators. Thereafter, the m6A methylation modification-based prognostic assessment pipeline (MPAP) was constructed to quantitatively assess GBM patients’ clinical prognosis combining the Robustness and LASSO regression. Single-sample gene-set enrichment analysis (ssGSEA) was used to estimate the specific immune cell infiltration level. We identified two diverse clusters with diverse m6A modification characteristics. Based on differentially expressed genes (DEGs) within two clusters, m6A-related signatures were identified to establish the MPAP, which can be used to quantitatively forecast the prognosis of GBM patients. In addition, the relationship between 21 m6A regulators and specific immune cell infiltration was demonstrated in our study and the m6A regulator ELAVL1 was determined to play an important role in the anticancer response to PD-L1 therapy. Our findings indicated the relationship between m6A methylation modification patterns and tumor microenvironment immune cell infiltration, through which we could comprehensively understand resistance to multiple therapies in GBM, as well as accomplish precise risk stratification according to m6A-related signatures.

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Identification of an Immune-Related Prognostic Signature Associated with Immune Infiltration in Melanoma

10.21203/rs.3.rs-21119/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nian Liu ◽

Zijian Liu ◽

Xinxin Liu ◽

Xiaoru Duan ◽

Yuqiong Huang ◽

...

Keyword(s):

Immune Cell ◽

Underlying Mechanism ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Immune Infiltration ◽

Mutation Burden ◽

Melanoma Prognosis ◽

Melanoma Patients ◽

Immune Related Genes

Abstract Background: Melanoma is the leading cause of cancer-related death among skin tumors, with an increasing incidence worldwide. Few studies have effectively investigated the significance of an immune-related genes (IRGs) signature for melanoma prognosis. Methods: Here, we constructed an IRGs prognostic signature using bioinformatics methods and evaluated and validated its predictive capability. Then, immune cell infiltration and tumor mutation burden (TMB) landscapes associated with this signature in melanoma were analyzed comprehensively. Results: With the 10-IRG prognostic signature, melanoma patients in the low-risk group showed better survival with distinct features of high immune cell infiltration and TMB. Importantly, melanoma patients in this subgroup were significantly responsive to MAGE-A3 in the validation cohort. Conclusions: This immune-related prognostic signature is thus a reliable tool to predict melanoma prognosis; as the underlying mechanism of this signature is associated with immune infiltration and mutation burden, it might reflect the benefit of immunotherapy to patients.

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