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Jie Cui,1– 4,* Hongbin Wang,5,6,* Shiyao Liu,1– 4 Yiming Zhao1– 4 1Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China; 2National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China; 3NHC Key Laboratory of Thrombosis and Hemostasis, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China; 4Collaborative Innovation Center of Hematology, Soochow University, Suzhou, 215006, People’s Republic of China; 5Department of Emergency, Jiangyin Hospital of Traditional Chinese Medicine, Wuxi, 214400, People’s Republic of China; 6Department of Intensive Care Unit, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yiming Zhao, Jiangsu Institute of Hematology, National Clinical Research Center for Hematologic Diseases, NHC Key Laboratory of Thrombosis and Hemostasis, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China, Tel +86-512-67781379, Fax +86-512-65113556, Email zhaoyimingbox@163.comBackground: Spontaneous intracerebral hemorrhage (ICH) is a subtype of stroke leading to high rates of morbidity and mortality in adults. Recent studies showed that immune and inflammatory responses might play essential roles in secondary brain injury. The purpose of this article was to provide a reference for further therapeutic strategies for ICH patients.Methods: GSE206971 and GSE216607 datasets from the gene expression omnibus (GEO) database were used to screen the highly immune-related differentally expressed genes (IRDEGs). We used the CIBERSORT algorithm to assess the level of immune signatures infiltration and got the possible function of IRDEGs which was analyzed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Protein-protein interaction (PPI) networks and six hub genes were identified in the Cytoscape plug-in. GSVA algorithm was performed to evaluate the potential pathways of six hub genes in ICH samples. The expression level of IL-7R chosen from six hub genes was further validated by Western blotting. The cell models of ICH were established for the research of IL-7/IL-7R signaling way.Results: A total of six hub genes (ITGAX, ITGAM, CCR2, CD28, SELL, and IL-7R) were identified. IL-7R was highly expressed in the mice ICH group, as shown by immunoblotting. Next, we constructed ICH cell models in RAW264.7 cells and BV2 cells. After treatment with IL-7, iNOS expression (M1 marker) was greatly inhibited while Arg-1(M2 marker) was enhanced, and it might function via the JAK3/STAT5 signaling pathway.Conclusion: The hypothesis is proposed that the IL-7/IL-7R signaling pathway might regulate the inflammatory process following ICH by regulating microglia polarization. Our study is limited and requires more in-depth experimental confirmation.Keywords: immune infiltration, intracerebral hemorrhage, CIBERSORT algorithm, IL-7 receptor

Jingwei Wang,1,2,* Bowen Xia,3,* Ruimin Ma,1 Qiao Ye1,2 1Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Occupational Medicine and Toxicology, Clinical Center for Interstitial Lung Diseases, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiao Ye, Department of Occupational Medicine and Toxicology, Clinical Center for Interstitial Lung Diseases, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Worker’s Stadium, Chaoyang District, Beijing, 100020, People’s Republic of China, Email yeqiao_chaoyang@sina.comPurpose: Chronic obstructive pulmonary disease (COPD) is the main cause of mortality world widely. Non-coding RNAs (lncRNAs) and associated competitive endogenous RNAs (ceRNAs) networks were recently proved to lead to mRNA gene expression downregulation but were still unclear in COPD. This study aims to investigate and elucidate the mechanisms underlying the involvement of ceRNA co-expression networks in COPD pathogenesis.Methods: Obtained expression signature of data from the Gene Expression Omnibus database and compared the differentially expression of mRNAs and miRNAs between COPD patients and healthy smokers. Predicted the miRNA–lncRNA and miRNA–mRNA interaction using online library and employed CIBERSORT to measure the proportions of the 22 immune cells in the COPD and control groups.Results: Established a ceRNA-network comprising 11 lncRNAs, 5 miRNAs, and 16 mRNAs. Using the weighted correlation network analysis method, we identified hub genes and hub miRNAs and obtained one core sub-network, XIST, FGD5-AS1, KCNQ1OT1, HOXA11-AS, LINC00667, H19, PRKCQ-AS1, NUTM2A-AS1/has-mir-454-3p/ZNF678, PRRG4. COPD patients had different proportions of immune cells than controls, and these variations were associated with the magnitude of pulmonary function parameters.Conclusion: The ceRNA-network, particularly the core sub-network, may be a putative goal for COPD, in which specific immune cells were involved.Keywords: chronic obstructive pulmonary disease, competing endogenous RNA network, microRNAs, miRNAs, CIBERSORT algorithm, gene expression omnibus

Abstract Background The tumor microenvironment (TME) in breast cancer plays a vital role in occurrence, development, and therapeutic responses. However, immune and stroma constituents in the TME are major obstacles to understanding and treating breast cancer. We evaluated the significance of TME-related genes in breast cancer. Methods Invasive breast cancer (BRCA) samples were retrieved from the TCGA and GEO databases. Stroma and immune scores of samples as well as the proportion of tumor infiltrating immune cells (TICs) were calculated using the ESTIMATE and CIBERSORT algorithms. TME-related differentially expressed genes (DEGs) were analyzed by a protein interaction (PPI) network and univariate Cox regression to determine CD1C as a hub gene. Subsequently, the prognostic value of CD1C, its response to immunotherapy, and its mechanism in the TME were further studied. Results In BRCA, DEGs were determined to identify CD1C as a hub gene. The expression level of CD1C in BRCA patients was verified based on the TCGA database, polymerase chain reaction (PCR) results, and western blot analysis. Immunohistochemical staining (IHC) results revealed a correlation between prognosis, clinical features, and CD1C expression in BRCA. Enrichment analysis of GSEA and GSVA showed that CD1C participates in immune-associated signaling pathways. CIBERSORT showed that CD1C levels were associated with tumor immune infiltrating cells (TILs), such as different kinds of T cells. Gene co-expression analysis showed that CD1C and the majority of immune-associated genes were co-expressed in BRCA. In renal cell carcinoma, patients with a high expression of CD1C had a better immunotherapy effect. Conclusion CD1C is an important part of the TME and participates in immune activity regulation in breast tumors. CD1C is expected to become a prognostic marker and a new treatment target for breast cancer.

Background: The tumor microenvironment (TME) in breast cancer plays a vital role in occurrence, development, and therapeutic responses. However, immune and stroma constituents in the TME are major obstacles to understanding and treating breast cancer. We evaluated the significance of TME-related genes in breast cancer. Methods: Invasive breast cancer (BRCA) samples were retrieved from the TCGA and GEO databases. Stroma and immune scores of samples as well as the proportion of tumor infiltrating immune cells (TICs) were calculated using the ESTIMATE and CIBERSORT algorithms. TME-related differentially expressed genes (DEGs) were analyzed by a protein interaction (PPI) network and univariate Cox regression to determine CD1C as a hub gene. Subsequently, the prognostic value of CD1C, its response to immunotherapy, and its mechanism in the TME were further studied. Results: In BRCA, DEGs were determined to identify CD1C as a hub gene. The expression level of CD1C in BRCA patients was verified based on the TCGA database, polymerase chain reaction (PCR) results, and western blot analysis. Immunohistochemical staining (IHC) results revealed a correlation between prognosis, clinical features, and CD1C expression in BRCA. Enrichment analysis of GSEA and GSVA showed that CD1C participates in immune-associated signaling pathways. CIBERSORT showed that CD1C levels were associated with tumor immune infiltrating cells (TILs), such as different kinds of T cells. Gene co-expression analysis showed that CD1C and the majority of immune-associated genes were co-expressed in BRCA. In renal cell carcinoma, patients with a high expression of CD1C had a better immunotherapy effect. Conclusion: CD1C is an important part of the TME and participates in immune activity regulation in breast tumors. CD1C is expected to become a prognostic marker and a new treatment target for breast cancer. [ABSTRACT FROM AUTHOR]

Objective: Although multiple hub genes have been identified in head and neck squamous cell cancer (HNSCC) in recent years, because of the limited sample size and inconsistent bioinformatics analysis methods, the results are not reliable. Therefore, it is urgent to use reliable algorithms to find new prognostic markers of HNSCC. Method: The Robust Rank Aggregation (RRA) method was used to integrate 8 microarray datasets of HNSCC downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). Later, Gene Ontology (GO) functional annotation together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out to discover functions of those discovered DEGs. According to the KEGG results, those discovered DEGs showed tight association with the occurrence and development of HNSCC. Then cibersort algorithm was used to analyze the infiltration of immune cells of HNSCC and we found that the main infiltrated immune cells were B cells, dendritic cells and macrophages. A protein-protein interaction (PPI) network was established; moreover, key modules were also constructed to select 5 hub genes from the whole network using cytoHubba. 3 hub genes showed significant relationship with prognosis for TCGA-derived HNSCC patients. Result: The potent DEGs along with hub genes were selected by the combined bioinformatic approach. AURKA, BIRC5 and UBE2C genes may be the potential prognostic biomarker and therapeutic targets of HNSCC. Conclusions: The Robust Rank Aggregation method and cibersort algorithm method can accurately predict the potential prognostic biomarker and therapeutic targets of HNSCC through multiple GEO datasets.

Di Chen, Lina Xiong, Li Zhang, Honglu Yu, Yushuang Xu, Mengmeng Wang, Xin Jiang, Zhifan Xiong Department of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaCorrespondence: Zhifan XiongDepartment of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430061, People’s Republic of ChinaTel +86 13517281937Email xiongzhifan@126.comPurpose: The tumor microenvironment (TME) plays a crucial role in the progression and prognosis of gastric cancer (GC). This study investigated TME-associated genes and explored their roles in the GC microenvironment.Methods: A total of 330 GC samples were extracted from TCGA. ESTIMATE and CIBERSORT algorithms were utilized to evaluate the stromal and immune scores of GC samples and the fraction of 22 immune cells infiltrated in the TME. Then, the TME-related differentially expressed genes (DEGs) were determined through integrative analysis. Protein-protein interaction (PPI) network and Cox regression analysis were conducted to analyze DEGs, and CSF1R was determined as the most crucial gene. We further probed the role of CSF1R in the GC microenvironment and evaluated the prognostic value of CSF1R.Results: We identified 560 TME-related DEGs and found CSF1R associated with the development and prognosis of GC. Further analysis showed that CSF1R was involved in immune-related signaling pathways. Furthermore, CIBERSORT analysis revealed that CSF1R expression correlated with several kinds of infiltrating immune cells, including tumor-associated macrophages (TAMs), B cells, NK cells, neutrophils, eosinophils, T cells, dendritic cells, and so on.Conclusion: In summary, CSF1R might take part in the modulation of immune-active status in the GC microenvironment and could be a promising biomarker for GC therapy and prognosis.Keywords: CSF1R, gastric cancer, tumor microenvironment, infiltrating immune cells, ESTIMATE algorithm, CIBERSORT algorithm

Background: This study aimed to investigate the tumor-related infiltrating lymphocytes (TILs) affecting the response of trastuzumab and identify potential biomarkers based on immune-related genes to improve prognosis and clinical outcomes of targeted therapies in breast cancer. Methods: Estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) was adopted to infer the fraction of stromal and immune cells through utilizing gene expression signatures in breast tumor samples. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) algorithm was applied to characterize cell composition of 22 lymphocytes from breast cancer tissues using their gene expression profiles. Immune-related genes were collected from the Immunology Database and Analysis (ImmPort). Univariate and multivariate Cox regression analyses were performed to identify the significant independent risk factors associated with poor overall survival (OS) and breast cancer-specific survival (BCSS) of breast cancer patients. Hub genes were identified based on the protein–protein interaction (PPI) network analysis. Results: Based on the ESTIMATE algorithm, a significant reduction of stromal scores was observed in tumor tissues and pretreated tumor tissues compared with nontumor and posttreated tumor tissues, respectively, while immune scores failed to present notably statistical differences between both groups. However, from the results of the univariate Cox regression analysis, the immune score was identified to be remarkably associated with the poor OS for breast cancer patients. Subsequently, the infiltrating lymphocytes were evaluated in tumor tissues based on the CIBERSORT algorithm. Furthermore, significance analysis identified 1,244 differentially expressed genes (DEGs) from the GSE114082 dataset, and then 91 overlapping immune-related DEGs were screened between GSE114082 and ImmPort datasets. Subsequently, 10 top hub genes were identified and five (IGF1, ADIPOQ, PPARG, LEP, and NR3C1) significantly correlated with worse OS and BCSS on response to trastuzumab in breast cancer patients. Conclusions: This study provided an insight into the immune score based on the tumor-related infiltrating lymphocytes in breast cancer tissues and demonstrates the benefits of immune infiltration on the treatment of trastuzumab. Meanwhile, the study established a novel five immune-related gene signature to predict the OS and BCSS of breast cancer treated by trastuzumab. [ABSTRACT FROM AUTHOR]

BackgroundThis study aimed to investigate the tumor-related infiltrating lymphocytes (TILs) affecting the response of trastuzumab and identify potential biomarkers based on immune-related genes to improve prognosis and clinical outcomes of targeted therapies in breast cancer.MethodsEstimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) was adopted to infer the fraction of stromal and immune cells through utilizing gene expression signatures in breast tumor samples. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) algorithm was applied to characterize cell composition of 22 lymphocytes from breast cancer tissues using their gene expression profiles. Immune-related genes were collected from the Immunology Database and Analysis (ImmPort). Univariate and multivariate Cox regression analyses were performed to identify the significant independent risk factors associated with poor overall survival (OS) and breast cancer-specific survival (BCSS) of breast cancer patients. Hub genes were identified based on the protein–protein interaction (PPI) network analysis.ResultsBased on the ESTIMATE algorithm, a significant reduction of stromal scores was observed in tumor tissues and pretreated tumor tissues compared with nontumor and posttreated tumor tissues, respectively, while immune scores failed to present notably statistical differences between both groups. However, from the results of the univariate Cox regression analysis, the immune score was identified to be remarkably associated with the poor OS for breast cancer patients. Subsequently, the infiltrating lymphocytes were evaluated in tumor tissues based on the CIBERSORT algorithm. Furthermore, significance analysis identified 1,244 differentially expressed genes (DEGs) from the GSE114082 dataset, and then 91 overlapping immune-related DEGs were screened between GSE114082 and ImmPort datasets. Subsequently, 10 top hub genes were identified and five (IGF1, ADIPOQ, PPARG, LEP, and NR3C1) significantly correlated with worse OS and BCSS on response to trastuzumab in breast cancer patients.ConclusionsThis study provided an insight into the immune score based on the tumor-related infiltrating lymphocytes in breast cancer tissues and demonstrates the benefits of immune infiltration on the treatment of trastuzumab. Meanwhile, the study established a novel five immune-related gene signature to predict the OS and BCSS of breast cancer treated by trastuzumab.

Background: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, accounting for high rates of morbidity and mortality in the population. The tumor microenvironment (TME), which plays a crucial role in GC progression, may serve as an optimal prognostic predictor of GC. In this study, we identified CXC motif chemokine receptor 4 (CXCR4) as a TME-related gene among thousands of differentially expressed genes (DEGs). We showed that CXCR4 can be used to predict the effect of immunotherapy in patients with GC.Methods: GC samples obtained from The Cancer Genome Atlas (TCGA) were analyzed for the presence of stroma (stromal score), the infiltration of immune cells (immune score) in tumor tissues, and the tumor purity (estimate score) using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm. DEGs were sorted based on differences in the values of the three scores. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the biological processes and pathways enriched in these DEGs. The correlations of scores with clinicopathological features and overall survival (OS) of patients with GC were assessed by the Kaplan–Meier survival and Cox regression analyses. Through subsequent protein–protein interaction (PPI) network and univariate Cox regression analyses, CXCR4 was identified as a TME-related gene. Gene Set Enrichment Analysis (GSEA) was performed to assess the role of CXCR4 in the TME of GC. The CIBERSORT algorithm was used to further explore the correlation between tumor-infiltrating immune cells (TIICs) and CXCR4. Finally, the TISIDB database was used to predict the efficacy of immunotherapy in patients with GC.Results: We extracted 1231 TME-related DEGs and by an overlapping screening of PPI network and univariate Cox regression, CXCR4 was identified as a biomarker of TME, which deeply engaged in immune-related biological processes of gastric cancer and have close association with several immunocompetent cells.Conclusion: CXCR4 may be a useful biomarker of prognosis and an indicator of the TME in GC.

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients.

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients. [ABSTRACT FROM AUTHOR]

Background Immune cells in the tumor microenvironment are an important prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, information on the heterogeneity and risk stratification of these cells is limited. We sought to develop a novel immune model to evaluate the prognostic intra-tumoral immune landscape of patients with DLBCL. Methods The ESTIMATE and CIBERSORT algorithms were used to estimate the numbers of 22 infiltrating immune cells based on the gene expression profiles of 229 patients with DLBCL who were recruited from a public database. The least absolute shrinkage and selection operator (Lasso) penalized regression analyses and nomogram model were used to construct and evaluate the prognostic immunoscore (PIS) model for overall survival prediction. An immune gene prognostic score (IGPS) was generated by Gene Set Enrichment Analysis (GSEA) and Cox regression analysis was and validated in an independent NCBI GEO dataset (GSE10846). Results A higher proportion of activated natural killer cells was associated with a poor outcome. A total of five immune cells were selected in the Lasso model and DLBCL patients with high PIS showed a poor prognosis (hazard ratio (HR) 2.16; 95% CI [1.33–3.50]; P = 0.002). Differences in immunoscores and their related outcomes were attributed to eight specific immune genes involved in the cytokine–cytokine receptor interaction and chemokine signaling pathways. The IGPS based on a weighted formula of eight genes is an independent prognostic factor (HR: 2.14, 95% CI [1.40–3.28]), with high specificity and sensitivity in the validation dataset. Conclusions Our findings showed that a PIS model based on immune cells is associated with the prognosis of DLBCL. We developed a novel immune-related gene-signature model associated with the PIS model and enhanced the prognostic functionality for the prediction of overall survival in patients with DLBCL.

To explore immune cell infiltration characteristics of, and hub genes associated with, hypertrophic cardiomyopathy (HCM). The GSE130036 dataset was downloaded and the differentially expressed genes (DEGs) were identified. The DEGs were analyzed via the CIBERSORT algorithm to understand the composition of 22 immune cell types between the HCM and normal myocardial tissue specimens. Weighted gene co-expression network analysis (WGCNA) was performed to segregate the DEGs into several modules and explore correlation between the key modules and specific immune cells enriched in the myocardial tissues of HCM patients. The biofunctional and disease enrichment of the genes among the modules was explored, and hub genes serving as potential biomarkers of HCM were identified. These genes were validated by GSE36961 dataset, and the discrimination ability was assessed by receiver operating characteristic curve analysis. CIBERSORT analysis showed that neutrophils and B-cells (naive and memory B-cells) were highly abundant in HCM samples, while macrophages (M0, M1, M2) were highly abundant in normal samples. WGCNA analysis of the DEGs yielded seven modules, and the gray and yellow modules were strongly associated with neutrophils and B-cells, and with macrophages, respectively. Yellow module genes were mainly functional in immune and inflammation processes. Gray module genes were mainly functional in the transportation of intercellular substances. SLITRK4 and CD163 showed a notably high area under the curve values in both datasets and may serve as potential biomarkers for HCM. SLITRK4 and CD163 may be promising Diagnostic Biomarkers of Hypertrophic Cardiomyopathy. The flowchart of the study. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, accounting for high rates of morbidity and mortality in the population. The tumor microenvironment (TME), which plays a crucial role in GC progression, may serve as an optimal prognostic predictor of GC. In this study, we identified CXC motif chemokine receptor 4 ( CXCR4 ) as a TME-related gene among thousands of differentially expressed genes (DEGs). We showed that CXCR4 can be used to predict the effect of immunotherapy in patients with GC. Methods: GC samples obtained from The Cancer Genome Atlas (TCGA) were analyzed for the presence of stroma (stromal score), the infiltration of immune cells (immune score) in tumor tissues, and the tumor purity (estimate score) using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm. DEGs were sorted based on differences in the values of the three scores. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the biological processes and pathways enriched in these DEGs. The correlations of scores with clinicopathological features and overall survival (OS) of patients with GC were assessed by the Kaplan-Meier survival and Cox regression analyses. Through subsequent protein-protein interaction (PPI) network and univariate Cox regression analyses, CXCR4 was identified as a TME-related gene. Gene Set Enrichment Analysis (GSEA) was performed to assess the role of CXCR4 in the TME of GC. The CIBERSORT algorithm was used to further explore the correlation between tumor-infiltrating immune cells (TIICs) and CXCR4. Finally, the TISIDB database was used to predict the efficacy of immunotherapy in patients with GC. Results: We extracted 1231 TME-related DEGs and by an overlapping screening of PPI network and univariate Cox regression, CXCR4 was identified as a biomarker of TME, which deeply engaged in immune-related biological processes of gastric cancer and have close association with several immunocompetent cells. Conclusion: CXCR4 may be a useful biomarker of prognosis and an indicator of the TME in GC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Gu, Gu, Xie, Chen, Xu and Fei.)

Objective: Although multiple hub genes have been identified in head and neck squamous cell cancer (HNSCC) in recent years, because of the limited sample size and inconsistent bioinformatics analysis methods, the results are not reliable. Therefore, it is urgent to use reliable algorithms to find new prognostic markers of HNSCC., Method: The Robust Rank Aggregation (RRA) method was used to integrate 8 microarray datasets of HNSCC downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). Later, Gene Ontology (GO) functional annotation together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out to discover functions of those discovered DEGs. According to the KEGG results, those discovered DEGs showed tight association with the occurrence and development of HNSCC. Then cibersort algorithm was used to analyze the infiltration of immune cells of HNSCC and we found that the main infiltrated immune cells were B cells, dendritic cells and macrophages. A protein-protein interaction (PPI) network was established; moreover, key modules were also constructed to select 5 hub genes from the whole network using cytoHubba. 3 hub genes showed significant relationship with prognosis for TCGA-derived HNSCC patients., Result: The potent DEGs along with hub genes were selected by the combined bioinformatic approach. AURKA, BIRC5 and UBE2C genes may be the potential prognostic biomarker and therapeutic targets of HNSCC., Conclusions: The Robust Rank Aggregation method and cibersort algorithm method can accurately predict the potential prognostic biomarker and therapeutic targets of HNSCC through multiple GEO datasets.

Immune infiltration in Prostate Cancer (PCa) was reported to be strongly associated with clinical outcomes. However, previous research could not elucidate the diversity of different immune cell types that contribute to the functioning of the immune response system. In the present study, the CIBERSORT method was employed to evaluate the relative proportions of immune cell profiling in PCa samples, adjacent tumor samples and normal samples. Three types of molecular classification were identified in tumor samples using the 'CancerSubtypes' package of the R software. Each subtype had specific molecular and clinical characteristics. In addition, functional enrichment was analyzed in each subtype. The submap and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were also used to predict clinical response to the immune checkpoint blockade. Moreover, the Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to screen for potential chemotherapeutic targets for the treatment of PCa. The results showed that Cluster I was associated with advanced PCa and was more likely to respond to immunotherapy. The findings demonstrated that differences in immune responses may be important drivers of PCa progression and response to treatment. Therefore, this comprehensive assessment of the 22 immune cell types in the PCa Tumor Environment (TEM) provides insights on the mechanisms of tumor response to immunotherapy and may help clinicians explore the development of new drugs., (© 2021 The Author(s).)

Background: Increasing studies suggest that tumor immune infiltration is a relative factor of prognosis in ovarian cancer (OvCa). The present study explored the composition of tumor-infiltrating immune cells (TIICs) in OvCa using CIBERSORT algorithm and further assessed their values for prognosis and therapeutic strategies by molecular subtypes., Methods: Publicly available databases including The Cancer Genome Atlas (TCGA) and GTEx were searched. Ovarian tumor samples were available from TCGA, and normal ovarian samples were obtained from the GTEx dataset. The relative proportions of immune cell profiling in OvCa and normal samples were evaluated by CIBERSORT algorithm. Association between each immune cell subtype and survival was inferred by the fractions of 22 immune cell types. "CancerSubtypes" R-package was employed to identify the three types of molecular classification and analyze the functional enrichment in each subclass. Response to immunotherapy and anticancer drug targets was predicted via TIDE algorithm and GDSC dataset., Results: Substantial variation reflecting individual difference was identified between cancer and normal tissues in the immune infiltration profiles. T cells CD4 memory activated, macrophages M1 were associated with improved overall survival (OS) as evaluated by univariate Cox regression and multivariate Cox. Three subtypes were identified by ´CancerSubtypes' R-package and every sub-cluster possessed specific immune cell characterization. Meanwhile, Cluster II exhibited poor prognosis and sensitive response to immunotherapy., Conclusions: The cellular component of immune infiltration shows remarkable variation in OvCa. Profiling of immune infiltration is useful in prediction of prognosis of OvCa. The results from profiling might be considered in therapeutic modulation., (© 2020 The Author(s).)

Background: Immune cells in the tumor microenvironment are an important prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, information on the heterogeneity and risk stratification of these cells is limited. We sought to develop a novel immune model to evaluate the prognostic intra-tumoral immune landscape of patients with DLBCL., Methods: The ESTIMATE and CIBERSORT algorithms were used to estimate the numbers of 22 infiltrating immune cells based on the gene expression profiles of 229 patients with DLBCL who were recruited from a public database. The least absolute shrinkage and selection operator (Lasso) penalized regression analyses and nomogram model were used to construct and evaluate the prognostic immunoscore (PIS) model for overall survival prediction. An immune gene prognostic score (IGPS) was generated by Gene Set Enrichment Analysis (GSEA) and Cox regression analysis was and validated in an independent NCBI GEO dataset (GSE10846)., Results: A higher proportion of activated natural killer cells was associated with a poor outcome. A total of five immune cells were selected in the Lasso model and DLBCL patients with high PIS showed a poor prognosis (hazard ratio (HR) 2.16; 95% CI [1.33-3.50]; P  = 0.002). Differences in immunoscores and their related outcomes were attributed to eight specific immune genes involved in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The IGPS based on a weighted formula of eight genes is an independent prognostic factor (HR: 2.14, 95% CI [1.40-3.28]), with high specificity and sensitivity in the validation dataset., Conclusions: Our findings showed that a PIS model based on immune cells is associated with the prognosis of DLBCL. We developed a novel immune-related gene-signature model associated with the PIS model and enhanced the prognostic functionality for the prediction of overall survival in patients with DLBCL., Competing Interests: The authors declare there are no competing interests., (©2020 Zhou et al.)

Tumor-infiltrating immune cells are closely related to the prognosis of bladder cancer. Analysis of tumor infiltrating immune cells is usually based on immunohistochemical analysis. Since many immune cell marker proteins are not specific for different immune cells, which may induce misleading or incomplete. CIBERSORT is an algorithm to estimate specific cell types in a mixed cell population using gene expression data. In this study, the CIBERSORT algorithm was used to identify the immune cell infiltration signatures. The gene expression profiles, mutation data, and clinical data were collected from The Cancer Genome Atlas (TCGA) database. Unsupervised consensus clustering was used to acquire the immune cell infiltration subtypes of bladder cancer based on the fractions of 22 immune cell types. Four immune cell clusters with different immune infiltrate and mutation characteristics were identified. In addition, this stratification has a prognostic relevance, with cluster 2 having the best outcome, cluster 1 the worst. These clusters showed distinct mRNA expression patterns. The characteristic genes in subtype cluster 1 were mainly involved in cell division, those in subtype cluster 2 were mainly related in antigen processing and presentation, those in subtype cluster 3 were mainly involved in epidermal cell differentiation, and those in subtype cluster 4 were mainly related in the humoral immune response. These differences may affect the development of the bladder cancer, the sensitivity to treatment as well as the prognosis. Through further validation, this study may contribute to the development of personalized therapy and precision medical treatments., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)