scholarly journals Comprehensive genomic analysis of microenvironment phenotypes in ovarian cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10255
Author(s):  
Jingshu Wang ◽  
Tingting Zhang ◽  
Lina Yang ◽  
Gong Yang
Keyword(s):  
Ovarian Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Classification Model ◽  
Immune Biomarkers ◽  
Cluster A ◽  
Cox Analysis

Background Ovarian cancer is one of the leading causes of cancer-related death in women. The incidence of ovarian cancer is insidious, and the recurrence rate is high. The survival rate of ovarian cancer has not significantly improved over the past decade. Recently, immune checkpoint inhibitors such as those targeting CTLA-4, PD-1, or PD-L1 have been used to treat ovarian cancer. Therefore, a full analysis of the immune biomarkers associated with this malignancy is necessary. Methods In this study, we used data from The Cancer Genome Atlas (TCGA) database to analyze the infiltration patterns of specific immune cell types in tumor samples. Data from the Gene Expression Omnibus (GEO) database was used for external validation. According to the invasion patterns of immune cells, we divided the ovarian cancer microenvironment into two clusters: A and B. These tumor microenvironment (TME) subtypes were associated with genomic and clinicopathological characteristics. Subsequently, a random forest classification model was established. Differential genomic features, functional enrichment, and DNA methylation were analyzed between the two clusters. The characteristics of immune cell infiltration and the expression of immune-related cytokines or markers were analyzed. Somatic mutation analysis was also performed between clusters A and B. Finally, multivariate Cox analysis was used to analyze independent prognostic factors. Results The ovarian cancer TME cluster A was characterized by less infiltration of immune cells and sparse distribution and low expression of immunomodulators. In contrast, cytotoxic T cells and immunosuppressive cells were significantly increased in the ovarian cancer TME cluster B. Additionally, immune-related cytokines or markers, including IFN-γ and TNF-β, were also expressed in large quantities. In total, 35 differentially methylated and expressed genes (DMEGs) were identified. Functional enrichment analyses revealed that the DMEGs in cluster B participated in important biological processes and immune-related pathways. The mutation load in cluster B was insignificantly higher than that of cluster A (p = 0.076). Multivariate Cox analysis showed that TME was an independent prognostic factor for ovarian cancer (hazard ratio: 1.33, 95% confidence interval: 1.01–1.75, p = 0.041). Conclusion This study described and classified basic information about the immune invasion pattern of ovarian cancer and integrated biomarkers related to different immunophenotypes to reveal interactions between ovarian cancer and the immune system.

scholarly journals Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer

2020 ◽  
pp. 153537022097202
Author(s):  
Xiaojun Liu ◽  
Jinghai Gao ◽  
Jing Wang ◽  
Jing Chu ◽  
Jiahao You ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Good Prognosis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Cancer Multiple

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in “iClusterPlus” R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time ( P <0.05) with activated IFN-gamma response, Wound Healing and Cytotoxic lymphocytes signal; 456 differentially expressed genes were acquired in C1 and C2 subtypes using limma (3.40.6) package, among which 419 were up-regulated and 37 were down-regulated, in TCGA dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis revealed that these genes were actively involved in ECM-receptor interaction, PI3K-Akt signaling pathway interaction KEGG pathway. Compared with the existing immune subtype, the Cluster2 sample showed a substantial increase in the proportion of the existing C2 immune subtype, accounting for 81.37%, which was associated with good prognosis. Our C1 subtype contains only 56.49% of the existing immune C1 and C4, which also explains the poor prognosis of C1. Furthermore, 52 immune-related lncRNAs were used to divide the TCGA-endometrial cancer and cervical cancer samples into two categories, and C2 had a good prognosis. The differentially expressed genes were highly correlated with immune-cell-related pathways. Based on lncRNA, two molecular subtypes of ovarian cancer were identified and had significant prognostic differences and immunological characteristics.

scholarly journals Identification of three molecular subtypes based on immune infiltration in ovarian cancer and its prognostic value

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Juan Liu ◽  
Zongjian Tan ◽  
Jun He ◽  
Tingting Jin ◽  
Yuanyuan Han ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Drug Targets ◽  
Immune Cell ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Classification ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Infiltration

Abstract 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.

scholarly journals Identification and development of a novel invasion-related gene signature for prognosis prediction in colon adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiahua Liu ◽  
Chunhui Jiang ◽  
Chunjie Xu ◽  
Dongyang Wang ◽  
Yuguang Shen ◽  
...  
Keyword(s):  
T Cells ◽  
External Validation ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Immune Infiltration ◽  
Prognosis Prediction ◽  
Protein Levels ◽  
Limma Package ◽  
Cox Analysis

AbstractThe overall survival of metastatic colon adenocarcinoma (COAD) remains poor, so it is important to explore the mechanisms of metastasis and invasion. This study aimed to identify invasion-related genetic markers for prognosis prediction in patients with COAD. Three molecular subtypes (C1, C2, and C3) were obtained based on 97 metastasis-related genes in 365 COAD samples from The Cancer Genome Atlas (TCGA). A total of 983 differentially expressed genes (DEGs) were identified among the different subtypes by using the limma package. A 6-gene signature (ITLN1, HOXD9, TSPAN11, GPRC5B, TIMP1, and CXCL13) was constructed via Lasso-Cox analysis. The signature showed strong robustness and could be used in the training, testing, and external validation (GSE17537) cohorts with stable predictive efficiency. Compared with other published signatures, our model showed better performance in predicting outcomes. Pan-cancer expression analysis results showed that ITLN1, TSPAN11, CXCL13, and GPRC5B were downregulated and TIMP1 was upregulated in most tumor samples, including COAD, which was consistent with the results of the TCGA and GEO cohorts. Western blot analysis and immunohistochemistry were performed to validate protein expression. Tumor immune infiltration analysis results showed that TSPAN11, GPRC5B, TIMP1, and CXCL13 protein levels were significantly positively correlated with CD4+ T cells, macrophages, neutrophils, and dendritic cells. Further, the TIMP1 and CXCL13 proteins were significantly related to the tumor immune infiltration of CD8+ T cells. We recommend using our signature as a molecular prognostic classifier to assess the prognostic risk of patients with COAD.

scholarly journals Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Binghao Zhao ◽  
Yuekun Wang ◽  
Yaning Wang ◽  
Congxin Dai ◽  
Yu Wang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Phenotype ◽  
Immune Phenotypes ◽  
Glioma Microenvironment ◽  
Genome Atlas

The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

scholarly journals Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

2020 ◽  
Author(s):  
Lili Fan ◽  
Han Lei ◽  
Ying Lin ◽  
Zhengwei Zhou ◽  
Guang Shu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Cell ◽  
Good Prognosis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prognosis ◽  
Immune Activity ◽  
Gene Set ◽  
Immune Infiltration ◽  
Keywords Ovarian Cancer

Abstract Background : Ovarian cancer (OC) is a serious tumor disease in gynecology. Many papers have reported that high tumor mutational burden (TMB) can generate many neoantigens to result in a higher degree of tumor immune infiltration, so our study aims to predict the key molecules in OC immunotherapy by combined TMB with immunoactivity-related gene. Method: We divided OC cases into two groups: the low & high TMB group hinged on the somatic mutation data from the Cancer Genome Atlas (TCGA). We also used single-sample gene set enrichment analysis (ssGSEA) scores of immune cell types to conduct unsupervised clustering of OC patients in the TCGA cohort and some of them were defined as the low & high immunity group. Besides, to further understand the function of these genes, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interaction network, survival prognosis analysis and immune infiltration analysis. Finally, the effects on prognosis and immunotherapy in OC patients were explored by the Group on Earth Observations verification the patients' responses to immunotherapy. Results: We found that the higher the TMB was associated with the higher OC grades. Moreover, both high TMB and high immunity were significantly correlated with a good prognosis of OC. Then, 14 up-regulated differential expression genes (Up-DEGs) that were closely related to the prognosis of OC patients were screened according to the high TMB group and the high immunity group. Next, pathway analysis revealed that Up-DGEs were mainly involved in immune response and T cell proliferation. Finally, four genes had a good prognosis and were validated in the GEO dataset which included CXCL13, FCRLA, PLA2G2D, and MS4A1. We also identified that four genes had a good prognosis in melanoma patients treated with anti-PD-L1 and anti-CTLA-4 in the TIDE database. Conclusion: High TMB can promote immune cell infiltration and increases immune activity. And our analysis also demonstrated that the higher the TMB, the higher the immune activity, the better the prognosis of OC. Altogether, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Keywords: ovarian cancer, TMB, immune cells infiltration, survival prognosis.

scholarly journals Identification of immune-based prostate cancer subtypes using mRNA expression

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.

scholarly journals TRPV4 is a Prognostic Biomarker that Correlates with the Immunosuppressive Microenvironment and Chemoresistance of Anti-Cancer Drugs

2021 ◽  
Vol 8 ◽  
Author(s):  
Kai Wang ◽  
Xingjun Feng ◽  
Lingzhi Zheng ◽  
Zeying Chai ◽  
Junhui Yu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Transient Receptor Potential ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Cancer Drugs ◽  
Free Interval ◽  
Anti Cancer ◽  
Pan Cancer

Background: Transient receptor potential cation channel subfamily V member 4 (TRPV4) has been reported to regulate tumor progression in many tumor types. However, its association with the tumor immune microenvironment remains unclear.Methods: TRPV4 expression was assessed using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The clinical features and prognostic roles of TRPV4 were assessed using TCGA cohort. Gene set enrichment analysis (GSEA) of TRPV4 was conducted using the R package clusterProfiler. We analyzed the association between TRPV4 and immune cell infiltration scores of TCGA samples downloaded from published articles and the TIMER2 database. The IC50 values of 192 anti-cancer drugs were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database and the correlation analysis was performed.Results: TRPV4 was highly expressed and associated with worse overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in colon adenocarcinoma (COAD) and ovarian cancer. Furthermore, TRPV4 expression was closely associated with immune regulation-related pathways. Moreover, tumor-associated macrophage (TAM) infiltration levels were positively correlated with TRPV4 expression in TCGA pan-cancer samples. Immunosuppressive genes such as PD-L1, PD-1, CTLA4, LAG3, TIGIT, TGFB1, and TGFBR1 were positively correlated with TRPV4 expression in most tumors. In addition, patients with high expression of TRPV4 might be resistant to the treatment of Cisplatin and Oxaliplatin.Conclusion: Our results suggest that TRPV4 is an oncogene and a prognostic marker in COAD and ovarian cancer. High TRPV4 expression is associated with tumor immunosuppressive status and may contribute to TAM infiltration based on TCGA data from pan-cancer samples. Patients with high expression of TRPV4 might be resistant to the treatment of Cisplatin and Oxaliplatin.

Tumor-associated immune cells and progression-free survival in advanced endometrial cancer (EC), results from the PHAEDRA trial (ANZGOG 1601).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 5584-5584
Author(s):  
Deborah Smith ◽  
Kristy Robledo ◽  
Sonia Yip ◽  
Michelle Cummins ◽  
Peey-Sei Kok ◽  
...  
Keyword(s):  
Immune Cells ◽  
Predictive Value ◽  
Tumor Response ◽  
External Validation ◽  
Prognostic Significance ◽  
Progression Free Survival ◽  
Immune Biomarkers ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
The Individual

5584 Background: Activity of durvalumab in patients with deficient mismatch repair (dMMR) advanced endometrial carcinoma (EC) was confirmed in the PHAEDRA trial (ANZGOG 1601). This study investigated the association between immune biomarkers and clinical outcomes in PHAEDRA. Methods: Formalin-fixed paraffin embedded sections immunohistochemically stained for PD-L1 using the Ventana platform, were with matched H&E slides scored independently by two pathologists according to the Ventana PD-L1 (SP263) algorithm for urothelial carcinoma (UC). Immune biomarkers assessed were PD-L1 staining of tumor cells (TCP) and immune cells (IC), and presence of tumor-associated immune cells (ICP). Results: Sixty-seven of the 71 patients had sufficient tumor for PD-L1 testing. AUC were 0.667, 0.726 and 0.644 for TCP, ICP and IC, respectively for predicting tumor response. Optimal cutpoints were TCP≥1%, ICP≥10% and IC≥35%. ICP≥10% achieved the highest sensitivity (53%) and specificity (82%) of the individual cutpoints. The optimal cutpoint algorithm was able to identify patients who would not respond, (sensitivity 88%, negative predictive value 92%), but had low specificity (48%) and positive predictive value (37%). Differences in PFS were found using ICP≥10% (logrank p = 0.01), compared to TCP (p = 0.25), IC (p = 0.48) and the UC algorithm (p = 0.08) (Figure 1). PFS was shorter in patients with pMMR than dMMR after adjusting for ICP (HR 2.99, 95%CI: 1.61-5.57, p < 0.001). Adjustment for MMR reduced the prognostic significance of ICP≥10% for PFS (HR 0.59, 95% CI: 0.28-1.23, p = 0.16). For OS, differences were seen for the UC algorithm (p = 0.02), but not ICP (p = 0.07), TCP (p = 0.18) or IC (p = 0.23). Similarly to PFS, adjustment for MMR reduced the prognostic significance of the UC algorithm for OS (HR: 0.53, 95% CI: 0.25-1.12, p = 0.10). Conclusions: In this exploratory analysis, ICP was more closely associated with tumor response and PFS than TCP or IC. ICP alone was better than the UC algorithm for predicting PFS. The optimum cutpoint algorithm was promising for identifying non-responders, but requires external validation. Clinical trial information: ACTRN12617000106336.

TRPV4 is a Prognostic Biomarker That Correlates With the Immunosuppressive Microenvironment and Chemoresistance of Anti-Cancer Drugs

2021 ◽  
Author(s):  
kai wang ◽  
Jun xing Feng ◽  
Zhi ling Zheng ◽  
Ying ze Chai ◽  
Hui jun Yu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Transient Receptor Potential ◽  
Immune Cell ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Receptor Potential ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Free Interval ◽  
Pan Cancer

Abstract Background: Transient receptor potential cation channel subfamily V member 4 (TRPV4) has been reported to regulate tumor progression in many tumor types. However, its association with the tumor immune microenvironment remains unclear.Methods: TRPV4 expression was assessed using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The clinical features and prognostic roles of TRPV4 were assessed using TCGA cohort. Gene set enrichment analysis (GSEA) of TRPV4 was conducted using the R package clusterProfiler. We analyzed the association between TRPV4 and immune cell infiltration scores of TCGA samples downloaded from published articles and the TIMER2 database.Results: TRPV4 was highly expressed and associated with worse overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in colon adenocarcinoma (COAD) and ovarian cancer. Furthermore, TRPV4 expression was closely associated with immune regulation-related pathways. Moreover, tumor-associated macrophage (TAM) infiltration levels were positively correlated with TRPV4 expression in TCGA pan-cancer samples. Immunosuppressive genes such as PD-L1, PD-1, CTLA4, LAG3, TIGIT, TGFB1, and TGFBR1 were positively correlated with TRPV4 expression in most tumors.Conclusions: Our results suggest that TRPV4 is an oncogene and a prognostic marker in COAD and ovarian cancer. High TRPV4 expression is associated with tumor immunosuppressive status and may contribute to TAM infiltration based on TCGA data from pan-cancer samples.

BATF2 and PDK4 As Diagnostic Molecular Markers of Sarcoidosis and Their Relationship With Immune Infiltration

2021 ◽  
Author(s):  
Xiaoyan Li ◽  
Jing Zhou ◽  
Jie He
Keyword(s):  
Immune Cells ◽  
Immune Cell ◽  
External Validation ◽  
Pulmonary Sarcoidosis ◽  
Gene Expression Omnibus ◽  
Diagnostic Markers ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Disorder ◽  
Qpcr Method

Abstract Background: Sarcoidosis (SA) is an immune disorder disease featured with granulomas formation. The work purposed to uncover potential markers for sarcoidosis (SA) diagnosis and explore how immune cell infiltration contributes to the pathogenesis of SA.Methods: Sarcoidosis GSE83456 samples and GSE42834 from Gene Expression Omnibus (GEO) were analyzed as the training and external validation sets, respectively. R statistical software was employed to uncover the differentially expressed genes (DEGs) of GSE83456. SVM algorithms and LASSO logistic regression were applied for screening and verification of the diagnostic markers for key module genes. The infiltration of immune cells in sarcoidosis patients’ blood samples was assessed by CIBERSORT. The expression of serum BATF2 and PDK4 was detected by RT-qPCR method, and the value of BATF2 and PDK4 mRNA expression in the diagnosis of pulmonary sarcoidosis was analyzed.Results: In total, 580 DEGs were identified from the key module. PDK4 (AUC=0.942) and BATF4 (AUC=0.980) were revealed as diagnostic markers of sarcoidosis. We found that monocytes, T cells regulatory (Tregs), mast cells, macrophages，NK cells, and dendritic cells may contribute to sarcoidosis development. In addition, PDK4 and BATF4 were closely associated with these immune cells. BATF2 and PDK4 were highly expressed in pulmonary sarcoidosis. BATF2 and PDK4 combined to predict the area under the ROC curve of pulmonary sarcoidosis was 0.922.Conclusions: PDK4 and BATF4 could be used as diagnostic markers of sarcoidosis, and immune cell infiltration severs an important role in sarcoidosis.

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