Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

10.21203/rs.3.rs-15489/v2 ◽

2020 ◽

Author(s):

Naiqiang Zhu ◽

Jingyi Hou

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cells ◽

Molecular Mechanisms ◽

Immune Cell ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

10.21203/rs.3.rs-15489/v1 ◽

2020 ◽

Author(s):

Naiqiang Zhu ◽

Jingyi Hou

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cells ◽

Molecular Mechanisms ◽

Immune Cell ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we aimed to characterize infiltrating immune cells and genes associated with the immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Gens and Genomes (KEGG) analysis were applied using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, which suggest novel molecular mechanisms of sarcoma metastasis.

Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

10.21203/rs.3.rs-15489/v4 ◽

2020 ◽

Author(s):

Naiqiang Zhu ◽

Jingyi Hou

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cells ◽

Molecular Mechanisms ◽

Immune Cell ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

Cancer Cell International ◽

10.1186/s12935-020-01672-3 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Naiqiang Zhu ◽

Jingyi Hou

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cells ◽

Molecular Mechanisms ◽

Immune Cell ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

Identification of the Key Serum Biomarkers to Diagnose and Predict Metastasis of Osteosarcoma Based on the Analysis of Immune Cell Infiltration Characteristics

10.21203/rs.3.rs-1047570/v1 ◽

2021 ◽

Author(s):

Zhihao Chen ◽

Liubing Li ◽

Ziyuan Li ◽

Xi Wang ◽

Mingxiao Han ◽

...

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cells ◽

Immune Cell ◽

Serum Biomarkers ◽

Differentially Expressed ◽

Cell Infiltration ◽

Diagnostic Model ◽

Differentially Expressed Mirnas

Abstract Background: The potential functions of circular RNAs (circRNAs) and micro RNAs (miRNAs) in osteosarcoma (OS) have not been fully elucidated. Especially, the behavior and mechanism of immune responses in OS development and progression have not been fully demonstrated. It was reported that circRNAs and miRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of many cancers. This study aimed to identify novel key serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics.Methods: The differentially-expressed circRNAs (DEcircRNAs), differentially-expressed miRNAs (DEmiRNAs),and differentially-expressed mRNAs (DEmRNAs) of human OS were investigated based on the microarray data downloaded from Gene Expression Omnibus (GEO) datasets. Then, we analyzed immune characteristics pattern of tumor-infiltrating immune cells in OS. On this basis, we identified statistically-significant transcription factors and performed pathway enrichment analysis. Subsequently, we constructed protein-protein interaction (PPI) and competitive endogenous RNA (ceRNA) networks. Moreover, the biological characteristic of targets in ceRNA networks was proposed. Finally, the expression and diagnostic capability of these potential biomarkers from ceRNA network were confirmed by RT-qPCR in patients’ serum.Results: Seven differentially-expressed circRNAs (DEcircRNAs), 166 differentially-expressed miRNAs (DEmiRNAs) and 175 differentially-expressed mRNAs (DEmRNAs) were identified in total. The highest level of infiltration in OS patients were M0 macrophages, M2 macrophages and CD8+ T cells. Further, M0 macrophages and CD8+ T cells were showed the largest negative correlation coefficients. These significant immune characteristics pattern of tumor-infiltrating immune cells were revealed by the principal component analysis in OS. Moreover, we found 185 statistically-significant transcription factors in which the main significant molecules show the potential in immunotherapy of OS. Hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A from ceRNA networks associated with immune cell infiltration were confirmed as the potential novel biomarkers for OS diagnosis, of which FAM98A could distinguish and predict metastasis. Most importantly, a novel diagnostic model consisting of the four promising biomarkers (hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A) was highlighted with 0.928 AUC value.Conclusions: In summary, the potenial serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics were found, and a novel diagnostic model consisting of four promising serum biomarkers was proposed firstly. These results provided a new perspective for the immunotherapy of OS.

Omics- and Pharmacogenomic Evidence for the Prognostic, Regulatory, and Immune-Related Roles of PBK in a Pan-Cancer Cohort

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.785370 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yi Liu ◽

Juan Xiang ◽

Gang Peng ◽

Chenfu Shen

Keyword(s):

Cell Cycle ◽

T Cells ◽

Cell Carcinoma ◽

Immune Cell ◽

Tumor Development ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Poor Overall Survival

PDZ-binding kinase (PBK) is known to regulate tumor progression in some cancer types. However, its relationship to immune cell infiltration and prognosis in different cancers is unclear. This was investigated in the present study by analyzing data from TCGA, GEO, GETx, TIMER, CPTAC, GEPIA2, cBioPortal, GSCALite, PROGNOSCAN, PharmacoDB, STRING, and ENCORI databases. PBK was overexpressed in most tumors including adenocortical carcinoma (hazard ratio [HR] = 2.178, p < 0.001), kidney renal clear cell carcinoma (KIRC; HR = 1.907, p < 0.001), kidney renal papillary cell carcinoma (HR = 3.024, p < 0.001), and lung adenocarcinoma (HR = 1.255, p < 0.001), in which it was associated with poor overall survival and advanced pathologic stage. PBK methylation level was a prognostic marker in thyroid carcinoma (THCA). PBK expression was positively correlated with the levels of BIRC5, CCNB1, CDC20, CDK1, DLGAP5, MAD2L1, MELK, PLK1, TOP2A, and TTK in 32 tumor types; and with the levels of the transcription factors E2F1 and MYC, which regulate apoptosis, the cell cycle, cell proliferation and invasion, tumorigenesis, and metastasis. It was also negatively regulated by the microRNAs hsa-miR-101-5p, hsa-miR-145-5p, and hsa-miR-5694. PBK expression in KIRC, liver hepatocellular carcinoma, THCA, and thymoma was positively correlated with the infiltration of immune cells including B cells, CD4+T cells, CD8+ T cells, macrophages, monocytes, and neutrophils. The results of the functional enrichment analysis suggested that PBK and related genes contribute to tumor development via cell cycle regulation. We also identified 20 drugs that potentially inhibit PBK expression. Thus, PBK is associated with survival outcome in a variety of cancers and may promote tumor development and progression by increasing immune cell infiltration into the tumor microenvironment. These findings indicate that PBK is a potential therapeutic target and has prognostic value in cancer treatment.

Expression, Prognosis, and Immune Infiltrates Analyses of E2Fs in Human Brain and CNS Cancer

BioMed Research International ◽

10.1155/2020/6281635 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Pan Liao ◽

Shuzhen Han ◽

Honglan Qu

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cell ◽

Expression Patterns ◽

Therapeutic Targets ◽

Functional Enrichment ◽

Cell Infiltration ◽

Immune Infiltration ◽

Cns Cancer

Objective. We investigated the expression patterns, potential functions, unique prognostic value, and potential therapeutic targets of E2Fs in brain and CNS cancer and tumor-infiltrating immune cell microenvironments. Methods. We analyzed E2F mRNA expression levels in diverse cancer types via Oncomine and GEPIA databases, respectively. Moreover, we evaluated the prognostic values using GEPIA database and TCGAportal database and the correlation of E2F expression with immune infiltration and the correlation between immune cell infiltration and GBM and LGG prognosis via TIMER database. Then, cBioPortal, GeneMANIA, and DAVID databases were used for mutation analysis, PPI network analysis of coexpressed gene, and functional enrichment analysis. Results. E2F1-8 expression increased in most cancers, including brain and CNS cancer. Higher expression in E2F1, 2, 4, 6, 7, and 8 indicated poor OS of LGG. Higher E2F3–6 and E2F1–8 expressions correlated with poor prognosis and increased immune infiltration levels in CD8+ T cells, macrophages, neutrophils, and DCs in GBM and CD8+ T cells, B cells, CD4+ T cells, neutrophils, macrophages, and DCs in LGG, respectively. Conclusion. E2F1–8 and E2F2–8 could be hopeful prognostic biomarkers of GBM and LGG, respectively. E2F3–6 and E2F1–8 could be likely therapeutic targets in patients with immune cell infiltration of GBM and LGG, respectively.

AHSA1 is a Prognostic Biomarker that Correlates with Macrophage Infiltration in Pan-Cancer

10.21203/rs.3.rs-746415/v1 ◽

2021 ◽

Author(s):

Fulei Li ◽

Chengdong Liu ◽

Yanling Chen ◽

Shasha Li ◽

Lu Bai

Keyword(s):

T Cells ◽

Cd8 T Cells ◽

Immune Cell ◽

Enrichment Analysis ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Cancer Data ◽

Pan Cancer

Abstract Background: Activator of heat shock 90 kDa protein ATPase homolog 1 (AHSA1) is differentially expressed in several tumor types. However, its association with immune cell infiltration remains elusive. Methods: AHSA1 expression was analyzed using The Cancer Genome Atlas (TCGA) pan-cancer data and normal tissue expression data from Genotype-Tissue Expression (GTEx). The clinical prognostic role of AHSA1 in pan-cancer was investigated, and an enrichment analysis of AHSA1 was performed using the R package “clusterProfiler.” We downloaded data regarding the immune cell infiltration level of TCGA pan-cancer tissues and analyzed the association between immune cell infiltration and AHSA1 expression. Results: The results of TCGA pan-cancer data analysis revealed that AHSA1 was overexpressed and associated with poor survival in patients with cancer. Furthermore, the infiltration levels of tumor-associated macrophages (TAMs) were higher, while those of CD8+ T cells were lower, in the high AHSA1 expression group. Conclusions: Our study suggests that AHSA1 is an oncogene and a risk factor for patient survival in cancer. AHSA1 may contribute to high infiltration levels of TAMs and low infiltration levels of CD8+ T cells, thus indicating that high AHSA1 expression may be associated with the tumor immunosuppressive microenvironment.

Identification of immune-based prostate cancer subtypes using mRNA expression

10.21203/rs.2.23038/v1 ◽

2020 ◽

Author(s):

Jukun Song ◽

Song He ◽

Wei Wang ◽

Jiaming Su ◽

Dongbo Yuan ◽

...

Keyword(s):

Prostate Cancer ◽

Immune Response ◽

Immune Cells ◽

Immune Cell ◽

Molecular Classification ◽

Cell Types ◽

Functional Enrichment ◽

Cell Infiltration ◽

Cell Subpopulation ◽

Immune Cell Infiltration

Abstract Background Immune infiltration of Prostate cancer (PCa) was highly related to clinical outcomes. However, previous works failed to elucidate the diversity of different immune cell types that make up the function of the immune response system. The aim of the study was to uncover the composition of TIICs in PCa utilizing the CIBERSORT algorithm and further reveal the molecular characteristics of PCa subtypes. Method In the present work, we employed the CIBERSORT method to evaluate the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We analyzed the correlation between immune cell infiltration and clinical information. The tumor-infiltrating immune cells of the TCGA PCa cohort were analyzed for the first time. The fractions of 22 immune cell types were imputed to determine the correlation between each immune cell subpopulation and clinical feature. Three types of molecular classification were identified via R-package of “CancerSubtypes”. The functional enrichment was analyzed in each subtype. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen chemotherapeutic targets for the potential treatment of PCa. Results In current work, we utilized the CIBERSORT algorithm to assess the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We investigated the correlation between immune cell infiltration and clinical data. The tumor-infiltrating immune cells in the TCGA PCa cohort were analyzed. The 22 immune cells were also calculated to determine the correlation between each immune cell subpopulation and survival and response to chemotherapy. Three types of molecular classification were identified. Each subtype has specific molecular and clinical characteristics. Meanwhile, Cluster I is defined as advanced PCa, and is more likely to respond to immunotherapy. Conclusions Our results demonstrated that differences in immune response may be important drivers of PCa progression and response to treatment. The deconvolution algorithm of gene expression microarray data by CIBERSOFT provides useful information about the immune cell composition of PCa patients. In addition, we have found a subtype of immunopositive PCa subtype and will help to explore the reasons for the poor effect of PCa on immunotherapy, and it is expected that immunotherapy will be used to guide the individualized management and treatment of PCa patients.

Bioinformatics analysis of the differentially co-expressed genes and immune cell infiltration features associated with pulmonary arterial hypertension

Archives of Medical Science ◽

10.5114/aoms/142102 ◽

2021 ◽

Author(s):

Wenshi Liu ◽

Dongdong Zheng ◽

Wenjing Lv ◽

Ying Hua ◽

Rong Huang ◽

...

Keyword(s):

Immune Cell ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Integrin Subunit ◽

Control Groups ◽

Immune Infiltration ◽

And Control

IntroductionThis study aimed to identify novel differentially co-expressed genes and to investigate the features of immune cell infiltration in PAH.Material and methodsThe GSE113439 and GSE117261 datasets were acquired from the Gene Expression Omnibus database. And the differentially expressed genes between PAH and control groups were identified based on the GSE117261 dataset. Weighted Gene Co-Expression Network Analysis (WGCNA) was adopted to analyze the pre-processed data. Functional enrichment analysis was then carried out to explore the biological functions of these genes modules. The differentially co-expressed key genes modules were in-depth verified by GEO2R analysis. The immune infiltration in PAH was investigated by Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT).ResultsWGCNA analysis found 15 differentially co-expressed genes modules, amongst which module blue indicated that it exhibited the strongest positive link to PAH, whereas module green presented the strongest negative association with PAH. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the genes in module blue were largely enriched in Lysosome, Complement, and coagulation cascades, and others, while the genes in module green were primarily enriched in the Chemokine signaling pathway, Platelet activation, etc. Integrin subunit alpha M (ITGAM) was identified as the differentially co-expressed key gene. Immune infiltration analysis by CIBERSORT showed that the differences between PAH and control groups or between PAH subgroups.ConclusionsITGAM was considered a promising biomarker to discriminate PAH from the control. Obvious differences were observed in immune infiltration between patients with PAH and normal groups.