Identification of Candidate Target Genes and Immune Cells in Oral Squamous Cell Carcinoma

Background. The advance of new treatment strategies for more effective management of oral cancer requires identification of novel biological targets. Therefore, the purpose of this study is to identify novel biomarkers associated with oral tumorigenesis and prognostic signature by comparing gene expression profile of oral squamous cell carcinomas (OSCCs). Methods. Four datasets including GSE25099, GSE30784, GSE37991, and GSE41613 were collected from Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Cox model analysis, identification of key genes, and Kaplan-Meier analysis were also performed. The xCell was utilized to analyze the infiltration levels of immune cells. Results. A total of 235 differentially expressed genes (DEGs) were found to be dysregulated in OSCC. These genes were mainly enriched in ECM receptor interaction and focal adhesion. Cox regression analysis identified 10 genes considered as key genes. Kaplan-Meier analysis showed that low expression of SERPINE1 (also known as PAI-1), high expression of CD1C, and C-X3-C motif chemokine receptor 1 (CX3CR1) were associated with well prognostic status in OSCC patients. In addition, we constructed a 3-immune-cell signature (myeloid dendritic cell, T cell CD4+ central memory, and common myeloid progenitor) that may be used to predict the survival status of OSCC patients. Conclusion. Three key genes and 3-immune-cell signature were potential biomarkers for the prognosis of OSCC, and they may serve as potential targets for the treatment of OSCC patients.

Download Full-text

IMUP and GPRC5A: Two Newly Identified Risk Score Indicators in Pancreatic Ductal Adenocarcinoma

10.21203/rs.3.rs-895660/v1 ◽

2021 ◽

Author(s):

Rong Wei ◽

Zixin Zeng ◽

Ningning Shen ◽

Ziyue Wang ◽

Honghong Shen ◽

...

Keyword(s):

Pancreatic Cancer ◽

Immune Cells ◽

Cox Regression ◽

Bioinformatic Analysis ◽

Cancer Development ◽

Local Hospital ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Key Genes ◽

The Difference

Abstract Background Pancreatic cancer has been a threateningly lethal malignant tumor worldwide. Despite the promising survival improvement in other cancer types attributing to the fast development of molecular precise medicine, the current treatment situation of pancreatic cancer is still woefully challenging since its limited response to neither traditional radiotherapy and chemotherapy nor emerging immunotherapy. The study is to explore potential responsible genes during the development of pancreatic cancer, thus identifying promising gene indicators and probable drug targets. Methods Different bioinformatic analysis were used to interpret the genetic events in pancreatic cancer development. Firstly, based on multiple cDNA microarray profiles from Gene Expression Omnibus (GEO) database, the genes with differently mRNA expression in cancer comparing to normal pancreatic tissues were identified, followed by being grouped based on the difference level. Then, GO and KEGG were performed to separately interpret the multiple groups of genes, and further Kaplan-Meier survival and Cox Regression analysis assisted us to scale down the candidate genes and select the potential key genes. Further, the basic physicochemical properties, the association with immune cells infiltration, mutation or other types variations besides expression gap in pancreatic cancer comparing to normal tissues of the selected key genes were analyzed. Moreover, the aberrant changed expression of key genes was validated by immunohistochemistry (IHC) experiment using local hospital tissue microarray samples and the clinical significance was explored based on TCGA clinical data. Results Firstly, a total of 22491 genes were identified to express differently in cancer comparing to normal pancreatic tissues based on 5 cDNA expression profiles, and the difference of 487/22491 genes was over 8-fold, and 55/487 genes were shared in multi profiles. Moreover, after genes interpretation which showed the >8-fold genes were mainly related to extracellular matrix structural constituent regulation, Kaplan-Meier survival and Cox-regression analysis were performed continually, and the result indicated that of the 55 extracellular locating genes, GPRC5A and IMUP were the only two independent prognostic indicators of pancreatic cancer. Further, detailed information of IMUP and GPRC5A were analyzed including their physicochemical properties, their expression and variation ratio and their association with immune cells infiltration in cancer, as well as the probable signaling pathways of genes regulation on pancreatic cancer development. Lastly, local IHC experiment performed on PAAD tissue array which was produced with 64 local hospital patients samples confirmed that GPRC5A and IMUP were abnormally up-regulated in pancreatic cancer, which directly associated with worse patients both overall (OS) and recurrence free survival (RFS). Conclusions Using multiple bioinformatic analysis as well as local hospital samples validation, we revealed that GPRC5A and IMUP expression were abnormally up-regulated in pancreatic cancer which associated statistical significantly with patients survival, and the genes’ biological features and clinical significance were also explored. However, more detailed experiments and clinical trials are obligatory to support their further potential drug-target role in clinical medical treatment.

Download Full-text

Identification of a five-gene prognostic signature related to B cells infiltration in pancreatic adenocarcinoma

10.21203/rs.3.rs-234348/v1 ◽

2021 ◽

Author(s):

Shaomei Tang ◽

Xiaoliang Huang ◽

Haixing Jiang ◽

Shanyu Qin

Keyword(s):

B Cells ◽

Pancreatic Adenocarcinoma ◽

Immune Cells ◽

Immune Cell ◽

Cox Regression ◽

Risk Scores ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cell Components

Abstract Background: Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue for elevating survival time of PAAD patients.Methods: The RNA sequencing and clinical data of PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrated immune cells and identified the immune cells closely related module. Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were used to construct a prognostic model. MCPcounter and EPIC were also applied to assess the immune cell components using gene expression profile.Results: The B cells closely related module was identified and five genes including ARID5A, CLEC2B, MICAL1, MZB1 and RAPGEF1 were ultimately selected to establish the prognostic signature for calculating risk scores of PAAD patients. Kaplan-Meier curves presented a worse survival in the high-risk patients (p<0.05) and the area under the Receiver operating characteristic (ROC) curve of risk score for 1-year and 3-year survival were 0.78 and 0.80 based on the training set. Also, similar results were verified in the validated and combined sets. Interestingly, low-risk group presented significantly elevated immune, stroma scores and proportion of B cells and associations between these five genes and B cells were identified by using multiple methods including ssGSEA, MCPcounter and EPIC. Conclusions: This is the first attempt to study a B cells related prognostic signature, which is instrumental in exploration of novel prognostic biomarkers in PAAD.

Download Full-text

IMUP and GPRC5A: two newly identified risk score indicators in pancreatic ductal adenocarcinoma

Cancer Cell International ◽

10.1186/s12935-021-02324-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Rong Wei ◽

Guoye Qi ◽

Zixin Zeng ◽

Ningning Shen ◽

Ziyue Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Immune Cells ◽

Cox Regression ◽

Bioinformatic Analysis ◽

Cancer Development ◽

Local Hospital ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Key Genes ◽

The Difference

Abstract Background Pancreatic cancer has been a threateningly lethal malignant tumor worldwide. Despite the promising survival improvement in other cancer types attributing to the fast development of molecular precise medicine, the current treatment situation of pancreatic cancer is still woefully challenging since its limited response to neither traditional radiotherapy and chemotherapy nor emerging immunotherapy. The study is to explore potential responsible genes during the development of pancreatic cancer, thus identifying promising gene indicators and probable drug targets. Methods Different bioinformatic analysis were used to interpret the genetic events in pancreatic cancer development. Firstly, based on multiple cDNA microarray profiles from Gene Expression Omnibus (GEO) database, the genes with differently mRNA expression in cancer comparing to normal pancreatic tissues were identified, followed by being grouped based on the difference level. Then, GO and KEGG were performed to separately interpret the multiple groups of genes, and further Kaplan–Meier survival and Cox Regression analysis assisted us to scale down the candidate genes and select the potential key genes. Further, the basic physicochemical properties, the association with immune cells infiltration, mutation or other types variations besides expression gap in pancreatic cancer comparing to normal tissues of the selected key genes were analyzed. Moreover, the aberrant changed expression of key genes was validated by immunohistochemistry (IHC) experiment using local hospital tissue microarray samples and the clinical significance was explored based on TCGA clinical data. Results Firstly, a total of 22,491 genes were identified to express differently in cancer comparing to normal pancreatic tissues based on 5 cDNA expression profiles, and the difference of 487/22491 genes was over eightfold, and 55/487 genes were shared in multi profiles. Moreover, after genes interpretation which showed the > eightfold genes were mainly related to extracellular matrix structural constituent regulation, Kaplan–Meier survival and Cox-regression analysis were performed continually, and the result indicated that of the 55 extracellular locating genes, GPRC5A and IMUP were the only two independent prognostic indicators of pancreatic cancer. Further, detailed information of IMUP and GPRC5A were analyzed including their physicochemical properties, their expression and variation ratio and their association with immune cells infiltration in cancer, as well as the probable signaling pathways of genes regulation on pancreatic cancer development. Lastly, local IHC experiment performed on PAAD tissue array which was produced with 62 local hospital patients samples confirmed that GPRC5A and IMUP were abnormally up-regulated in pancreatic cancer, which directly associated with worse patients both overall (OS) and recurrence free survival (RFS). Conclusions Using multiple bioinformatic analysis as well as local hospital samples validation, we revealed that GPRC5A and IMUP expression were abnormally up-regulated in pancreatic cancer which associated statistical significantly with patients survival, and the genes’ biological features and clinical significance were also explored. However, more detailed experiments and clinical trials are obligatory to support their further potential drug-target role in clinical medical treatment.

Download Full-text

Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

10.21203/rs.3.rs-472194/v1 ◽

2021 ◽

Author(s):

Wenxiang Zhang ◽

Bolun Ai ◽

Xiangyi Kong ◽

Xiangyu Wang ◽

Jie Zhai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Homologous Recombination ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

Cox Regression Analysis ◽

Homologous Recombination Deficiency ◽

Ajcc Stage ◽

Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

Download Full-text

GJA1 Expression and Its Prognostic Value in Cervical Cancer

BioMed Research International ◽

10.1155/2020/8827920 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Silu Meng ◽

Xinran Fan ◽

Jianwei Zhang ◽

Ran An ◽

Shuang Li

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Logistic Regression ◽

Gap Junction ◽

Signaling Pathway ◽

Immune Cells ◽

Immune Cell ◽

Normal Tissues ◽

Kaplan Meier ◽

Chi Squared

Gap Junction Protein Alpha 1 (GJA1) belongs to the gap junction family and has been widely studied in cancers. We evaluated the role of GJA1 in cervical cancer (CC) using public data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The difference of GJA1 expression level between CC and normal tissues was analyzed by the Gene Expression Profiling Interactive Analysis (GEPIA), six GEO datasets, and the Human Protein Atlas (HPA). The relationship between clinicopathological features and GJA1 expression was analyzed by the chi-squared test and the logistic regression. Kaplan–Meier survival analysis and Cox proportional hazard regression analysis were used to assessing the effect of GJA1 expression on survival. Gene set enrichment analysis (GSEA) was used to screen the signaling pathways regulated by GJA1. Immune Cell Abundance Identifier (ImmuCellAI) was chosen to analyze the immune cells affected by GJA1. The expression of GJA1 in CC was significantly lower than that in normal tissues based on the GEPIA, GEO datasets, and HPA. Both the chi-squared test and the logistic regression showed that high-GJA1 expression was significantly correlated with keratinization, hormone use, tumor size, and FIGO stage. The Kaplan–Meier curves suggested that high-GJA1 expression could indicate poor prognosis ( p = 0.0058 ). Multivariate analysis showed that high-GJA1 expression was an independent predictor of poor overall survival (HR, 4.084; 95% CI, 1.354-12.320; p = 0.013 ). GSEA showed many cancer-related pathways, such as the p53 signaling pathway and the Wnt signaling pathway, were enriched in the high-GJA1-expression group. Immune cell abundance analysis revealed that the abundance of CD8 naive, DC, and neutrophil was significantly increased in the high-GJA1-expression group. In conclusion, GJA1 can be regarded as a potential prognostic marker of poor survival and therapeutic target in CC. Moreover, many cancer-related pathways may be the critical pathways regulated by GJA1. Furthermore, GJA1 can affect the abundance of immune cells.

Download Full-text

Tumor Infiltration by OX40+ Cells Enhances the Prognostic Significance of CD16+ Cell Infiltration in Colorectal Cancer

Cancer Control ◽

10.1177/1073274820903383 ◽

2020 ◽

Vol 27 (1) ◽

pp. 107327482090338

Author(s):

Fabian Haak ◽

Isabelle Obrecht ◽

Nadia Tosti ◽

Benjamin Weixler ◽

Robert Mechera ◽

...

Keyword(s):

Colorectal Cancer ◽

Immune Cell ◽

Cox Regression ◽

Prognostic Significance ◽

Tumor Necrosis Factor Receptor ◽

Tissue Microarrays ◽

Cell Infiltration ◽

Prognostic Impact ◽

Cox Regression Analysis ◽

Kaplan Meier

Objectives: Analysis of tumor immune infiltration has been suggested to outperform tumor, node, metastasis staging in predicting clinical course of colorectal cancer (CRC). Infiltration by cells expressing OX40, a member of the tumor necrosis factor receptor family, or CD16, expressed by natural killer cells, monocytes, and dendritic cells, has been associated with favorable prognosis in patients with CRC. We hypothesized that assessment of CRC infiltration by both OX40+ and CD16+ cells might result in enhanced prognostic significance. Methods: Colorectal cancer infiltration by OX40 and CD16 expressing cells was investigated in 441 primary CRCs using tissue microarrays and specific antibodies, by immunohistochemistry. Patients’ survival was evaluated by Kaplan-Meier and log-rank tests. Multivariate Cox regression analysis, hazard ratios, and 95% confidence intervals were also used to evaluate prognostic significance of OX40+ and CD16+ cell infiltration. Results: Colorectal cancer infiltration by OX40+ and CD16+ cells was subclassified into 4 groups with high or low infiltration levels in all possible combinations. High levels of infiltration by both OX40+ and CD16+ cells were associated with lower pT stage, absence of peritumoral lymphocytic (PTL) inflammation, and a positive prognostic impact. Patients bearing tumors with high infiltration by CD16+ and OX40+ cells were also characterized by significantly longer overall survival, as compared with the other groups. These results were confirmed by analyzing an independent validation cohort. Conclusions: Combined infiltration by OX40+ and CD16+ immune cells is an independent favorable prognostic marker in CRC. The prognostic value of CD16+ immune cell infiltration is significantly improved by the combined analysis with OX40+ cell infiltration.

Download Full-text

Signature Based on Immune-Related LncRNA Can Predict Overall Survival of Osteosarcoma Patients

10.21203/rs.3.rs-45370/v1 ◽

2020 ◽

Author(s):

Longqing Li ◽

Lianghao Zhang ◽

Manhas Adbul Khader ◽

Yan Zhang ◽

Xinchang Lu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Immune Checkpoint ◽

High Throughput Sequencing ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Checkpoint Gene ◽

Cox Regression Analysis

Abstract Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma.Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated.Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy.Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma.

Download Full-text

Profiles of immune infiltration and its relevance to survival outcome in meningiomas

Bioscience Reports ◽

10.1042/bsr20200538 ◽

2020 ◽

Vol 40 (5) ◽

Author(s):

Xiaodong Chen ◽

Fen Tian ◽

Peng Lun ◽

Yugong Feng

Keyword(s):

Immune Cells ◽

Clustering Analysis ◽

Immune Cell ◽

Cox Regression ◽

Cell Types ◽

Survival Outcome ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Online Databases ◽

The Relationship

Abstract Tumor-infiltrating immune cells play a decisive part in prognosis and survival. Until now, previous researches have not made clear about the diversity of cell types involved in the immune response. The objective of this work was to confirm the composition of tumor-infiltrating immune cells and their correlation with prognosis in meningiomas based on a metagene approach (known as CIBERSORT) and online databases. A total of 22 tumor-infiltrating immune cells were detected to determine the relationship between the immune infiltration pattern and survival. The proportion of M2 macrophages was more abundant in 68 samples, reaching more than 36%. Univariate Cox regression analysis displayed that the proportion of dendritic cells was obviously related to prognosis. Hierarchical clustering analysis identified two clusters by the method of within sum of squares errors, which exhibited different infiltrating immune cell composition and survival. To summarize, our results indicated that proportions of tumor-infiltrating immune cells as well as cluster patterns were associated with the prognosis, which offered clinical significance for research of meningiomas.

Download Full-text

Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

Frontiers in Oncology ◽

10.3389/fonc.2020.555632 ◽

2021 ◽

Vol 10 ◽

Author(s):

Liang Zhao ◽

Jiayue Zhang ◽

Zhiyuan Liu ◽

Yu Wang ◽

Shurui Xuan ◽

...

Keyword(s):

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Malignant Tumors ◽

Cox Model ◽

Molecular Classification ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Alternative Mrna Splicing

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

Download Full-text

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

Frontiers in Immunology ◽

10.3389/fimmu.2021.685213 ◽

2021 ◽

Vol 12 ◽

Author(s):

Saadia Ait Ssi ◽

Dounia Chraa ◽

Khadija El Azhary ◽

Souha Sahraoui ◽

Daniel Olive ◽

...

Keyword(s):

Gene Expression ◽

Immune Cells ◽

Cox Regression ◽

Gene Expression Signature ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Tumor Escape ◽

Cox Regression Analysis ◽

Patient Prognosis ◽

Cluster 2

BackgroundGlioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.MethodsWe integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.ResultsOur study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.ConclusionOur work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.

Download Full-text