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

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.

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

10.21203/rs.3.rs-15489/v3 ◽

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 ◽

Immune Cell Infiltration ◽

Blue Module

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.

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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 ◽

Immune Cell Infiltration ◽

Blue Module

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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 ◽

Immune Cell Infiltration ◽

Blue Module

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.

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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 ◽

Immune Cell Infiltration ◽

Blue Module

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.

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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 ◽

Immune Cell Infiltration ◽

Blue Module

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.

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Immune Cell Infiltration as Signatures for the Diagnosis and Prognosis of Malignant Gynecological Tumors

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.702451 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qi-Fang Liu ◽

Zi-Yi Feng ◽

Li-Li Jiang ◽

Tong-Tong Xu ◽

Si-Man Li ◽

...

Keyword(s):

Nk Cells ◽

Immune Cells ◽

Immune Cell ◽

Cell Infiltration ◽

Diagnostic Model ◽

Immune Cell Infiltration ◽

Consensus Clustering ◽

Strong Basis ◽

Gynecological Tumors ◽

Diagnosis And Prognosis

Background Malignant gynecological tumors are the main cause of cancer-related deaths in women worldwide and include uterine carcinosarcomas, endometrial cancer, cervical cancer, ovarian cancer, and breast cancer. This study aims to determine the association between immune cell infiltration and malignant gynecological tumors and construct signatures for diagnosis and prognosis.Methods We acquired malignant gynecological tumor RNA-seq transcriptome data from the TCGA database. Next, the “CIBERSORT” algorithm calculated the infiltration of 22 immune cells in malignant gynecological tumors. To construct diagnosis and prognosis signatures, step-wise regression and LASSO analyses were applied, and nomogram and immune subtypes were further identified.Results Notably, Immune cell infiltration plays a significant role in tumorigenesis and development. There are obvious differences in the distribution of immune cells in normal, and tumor tissues. Resting NK cells, M0 Macrophages, and M1 Macrophages participated in the construction of the diagnostic model, with an AUC value of 0.898. LASSO analyses identified a risk signature including T cells CD8, activated NK cells, Monocytes, M2 Macrophages, resting Mast cells, and Neutrophils, proving the prognostic value for the risk signature. We identified two subtypes according to consensus clustering, where immune subtype 3 presented the highest risk.Conclusion We identified diagnostic and prognostic signatures based on immune cell infiltration. Thus, this study provided a strong basis for the early diagnosis and effective treatment of malignant gynecological tumors.

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Five Genes as Diagnostic Markers of Ischemic Stroke and Their Correlation with Immune Infiltration

10.21203/rs.3.rs-936536/v1 ◽

2021 ◽

Author(s):

Meng Wang ◽

Ruijie Zhang ◽

Qiongfeng Guan ◽

Yindan Yao ◽

Liyuan Han

Keyword(s):

T Cells ◽

Dendritic Cells ◽

Ischemic Stroke ◽

Immune Cells ◽

Immune Cell ◽

Diagnostic Markers ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Γδt Cells ◽

Negatively Associated

Abstract Background: This study aimed to identify potential diagnostic markers of ischemic stroke (IS) and discuss the function of immune cell infiltration during the pathological process. Methods: We used IS datasets from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified, and functional correlation analysis was performed. We then screened and verified the diagnostic markers of IS. We evaluated the infiltration of immune cells in infarcts using CIBERSORT and analyzed the correlation between diagnostic markers and infiltrating immune cells. Results: A total of 366 DEGs were screened in this study. Genes encoding CTSG, F13A1, PABPC1, ECHDC2, BIRC2 and infiltrating monocytes, M0 macrophages, activated dendritic cells, and neutrophils (area under the curve [AUC] = 0.945) were identified as diagnostic markers of IS. Immune cell infiltration analysis suggested that memory B cells, regulatory T cells, M0 macrophages, CD8 + T cells, γδT cells, activated natural killer cells, monocytes, activated mast cells, and neutrophils were involved in the IS process. Analysis of correlations between expressed genes and infiltrating immune cells found that CTSG was positively associated with M0 macrophages, F13A1 was positively associated with monocytes, PABPC1 was positively associated with activated dendritic cells, eosinophils were negatively associated with neutrophils, ECHDC2 was negatively associated with monocytes, and BIRC2 was positively associated with eosinophils. Conclusion: five genes and four types of immune cells were identified as diagnostic markers of IS, and immune cell infiltration may play an important role in the progression of IS.

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Identification and validation of methylated differentially expressed miRNAs and immune infiltrate profile in EBV-associated gastric cancer

Clinical Epigenetics ◽

10.1186/s13148-020-00989-0 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Mansheng Zhu ◽

Qixiang Liang ◽

Tao Chen ◽

Qian Kong ◽

Gengtai Ye ◽

...

Keyword(s):

Gastric Cancer ◽

Dna Methylation ◽

Immune Cell ◽

Differentially Expressed ◽

Research Network ◽

Cell Infiltration ◽

Migration And Invasion ◽

Pathway Enrichment Analysis ◽

Immune Cell Infiltration ◽

Differentially Expressed Mirnas

Abstract Background The recent discovery of cancer/tissue specificity of miRNA has indicated its great potential as a therapeutic target. In Epstein–Barr virus-associated gastric cancer (EBVaGC), host genes are affected by extensive DNA methylation, including miRNAs. However, the role of methylated miRNA in the development of EBVaGC and immune cell infiltration has largely remained elusive. Results After crossmatching the DNA methylation and expression profile of miRNA and mRNA in the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas Research Network (TCGA), we discovered that miR-129-2-3p was significantly suppressed due to hypermethylation on its enhancer in EBVaGC. The differentially expressed genes (DEGs) added up to 30, among which AKAP12 and LARP6 were predicted to be the target genes of miR-129-2-3p and negatively correlated with patients’ survival. Accordingly, miR-129-2-3p was significantly down-regulated in tumor samples in 26 (65%) out of 40 cases in our cohort (P < 0.0001). The proliferation, migration and invasion functions of GC cells were significantly promoted when transfected with miR-129-2-3p inhibitor and suppressed when transfected with mimics or treated with 5-aza-2′-deoxycytidine. Moreover, a comprehensive regulation network was established by combining the putative transcription factors, miRNA-mRNA and protein–protein interaction (PPI) analysis. Pathway enrichment analysis showed that cytokine activity, especially CCL20, was the most prominent biological process in EBVaGC development. Immune cell infiltration analysis demonstrated CD4+ T cell, macrophage and dendritic cell infiltrates were significantly enriched for the prognostic-indicated hub genes. Conclusion This study has provided a comprehensive analysis of differentially expressed miRNAs and mRNAs associated with genome-wide DNA methylation by integrating multi-source data including transcriptome, methylome and clinical data from GEO and TCGA, QPCR of tumor samples and cell function assays. It also gives a hint on the relationships between methylated miRNA, DEGs and the immune infiltration. Further experimental and clinical investigations are warranted to explore the underlying mechanism and validate our findings.

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Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma

PeerJ ◽

10.7717/peerj.9996 ◽

2020 ◽

Vol 8 ◽

pp. e9996

Author(s):

Yongyong Wang ◽

Jianji Guo

Keyword(s):

T Cells ◽

Immune Cells ◽

Immune Cell ◽

Smoking History ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Squamous Cell Lung Carcinoma ◽

Cell Lung Carcinoma ◽

Tfh Cells ◽

Memory Cd4 T Cells

Background Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history. Methods The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical characteristics was explored. According to the level of immune cell infiltration, LUSC patients with smoking history were divided into high or low group to screen the differentially expressed lncRNAs and mRNAs. The prediction of target genes was performed by miRanda. Finally, the prognostic value of a certain signature was confirmed in an independent dataset. Results Higher abundance of tumor-infiltrating T follicular helper (Tfh) cells together with a lower abundance of resting memory CD4 T cells had been found in LUSC current reformed smokers for ≤15 years and current smoking patients. Moreover, Tfh cell infiltration was not only associated with better overall survival (OS) but also varied from different degrees of TNM stage. Low expression of lncRNA PWRN1 and its potential regulating genes DMRTB1, PIRT, APOBEC1, and ZPBP2 were associated with better OS. Combining PWRN1 and four regulating genes as a signature, patients with higher-level expression of the signature had shorter survival time in not only the TCGA but also in the GEO dataset. Conclusions It was found that Tfh cells presented higher infiltration in LUSC current reformed smokers for ≤15 years and current smokers, while resting memory CD4 T cells had lower infiltration. The signature consisting of PWRN1 as well as its predicted targeted mRNAs was dysregulated in different levels of Tfh cell infiltration and might indicate patients’ OS.

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Activation of MAPK/ERK signalling in Merlin-null Schwann cells leads to increased and sustained immune cell infiltration in the peripheral nervous system

Neuro-Oncology ◽

10.1093/neuonc/noab195.017 ◽

2021 ◽

Vol 23 (Supplement_4) ◽

pp. iv8-iv8

Author(s):

Evyn Woodhouse ◽

Liyam Laraba ◽

Charlotte Lespade ◽

Marie Srotyr ◽

Alison C Lloyd ◽

...

Keyword(s):

T Cells ◽

Schwann Cells ◽

Immune Cells ◽

Peripheral Nerves ◽

Dorsal Root ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Erk Pathway ◽

Raf Kinase

Abstract Aims Previous work has shown that increased numbers of macrophages are associated with more rapid schwannoma tumour growth and we are interested in signals that control entry of macrophages and other immune cells into these tumours. Activation of the Raf-kinase domain and the Raf/MEK/ERK pathway within Schwann cells has been observed to induce an inflammatory response in peripheral nerves in the absence of injury. Activation of an inducible Raf-kinase transgene in Schwann cells allows modelling of acute demyelination of peripheral nerves without nerve injury. This Raf-oestrogen receptor fusion protein (Raf-TR) is activated by the oestrogen analogue Tamoxifen and so allows targeted, controlled activation of the Raf/MEK/ERK pathway within the Schwann cells. Here, in order to understand drivers of tumour formation, we assess the effect of MAPK activation in Merlin-null Schwann cells upon immune cell infiltration within the PNS. Method RafTR-P0CRE-NF2fl/fl mice of 4-6 weeks age were injected daily (IP) with 2mg of 4-hydroxy-tamoxifen or vehicle (corn oil) control for 5 consecutive days. RafTR was activated on either a Merlin (NF2) wild-type (NF2 fl/fl, P0-CRE-) or NF2 null (NF2 fl/fl, P0-CRE+) background and effects on immune cell infiltration studied in each condition. Immunofluorescence was performed in the dorsal root ganglia (DRGs) and sciatic nerves of mice to identify various immune cell infiltrates at various timepoints. These will include neutrophils, mast cells, T-Cells and macrophages using the cell markers Csf3r, C-kit, CD3 and IBA1 respectively. Results At 21 days post treatment, a significantly increased infiltration of macrophages within the sciatic nerve and dorsal root ganglia was observed in mice treated with Tamoxifen when compared to vehicle controls. Loss of NF2 led to a massive increase in the number of macrophages recruited to peripheral nerves in tamoxifen-treated mice compared to Cre- mice and Cre+ treated with vehicle alone. Further assessment of other immune cell infiltration including neutrophils, mast cells and T cells are ongoing. Conclusion Raf/MEK/ERK signalling, in the absence of tumour suppressor Merlin, significantly increases the infiltration of inflammatory cells such as macrophages into peripheral nerves even in the absence of injury. As this effect is enhanced in NF2 null mice, this suggests that Merlin plays an important role in inhibiting the inflammatory response in peripheral nerves. It also suggests that Merlin could be involved in maintaining the blood nerve barrier (BNB), as in its absence the greater influx of immune cells into the nerves and DRGs suggests a more complete loss of BNB function than just activation of the Raf/MEK/ERK cascade alone.

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