scholarly journals Identification of recurrence marker associated with immune infiltration in prostate cancer with radical resection and build prognostic nomogram

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xin Rui ◽  
Siliang Shao ◽  
Li Wang ◽  
Jiangyong Leng
Keyword(s):  
Prostate Cancer ◽  
Immune Cells ◽  
Prediction Models ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Recurrence ◽  
Th2 Cells ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Prostate Cancer Recurrence

Abstract Background Some historic breakthroughs have been made in immunotherapy of advanced cancer. However, there is still little research on immunotherapy in prostate cancer. We explored the relationship between immune cell infiltration and prostate cancer recurrence and tried to provide new ideas for the treatment of prostate cancer. Methods Prostate cancer RNA-seq data and clinical information were downloaded from the TCGA database and GEO database. The infiltration of 24 immune cells in tissues was quantified by ssGSEA. Univariate Cox regression analysis was used to screen for immune cell types associated with tumor recurrence, weighted gene co-expression network analysis (WGCNA) and LASSO were used to identify hub genes which regulate prognosis in patients through immune infiltration. Then, the nomogram was constructed based on the hub gene to predict the recurrence of prostate cancer, and the decision curve analysis (DCA) was used to compare the accuracy with the PSA and Gleason prediction models. Result Analysis showed that Th2 cells and Tcm related to prostate cancer recurrence after radical prostatectomy, and they are independent protective factors for recurrence. Through WGCNA and Lasso, we identified that NDUFA13, UQCR11, and USP34 involved in the infiltration of Th2 cells and Tcm in tumor tissues, and the expression of genes is related to the recurrence of patients. Based on the above findings, we constructed a clinical prediction model and mapped a nomogram, which has better sensitivity and specificity for prostate cancer recurrence prediction, and performed better in comparison with PSA and Gleason’s predictions. Conclusion The immune cells Th2 cells and Tcm are associated with recurrence of PCa. Moreover, the genes NDUFA13, UQCR11, and USP34 may affect the recurrence of PCa by affecting the infiltration of Th2 cells and Tcm. Moreover, nomogram can make prediction effectively.

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

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xiaodong Chen ◽  
Fen Tian ◽  
Peng Lun ◽  
Yugong Feng
Keyword(s):  
Immune Cells ◽  
Clustering Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Cell Types ◽  
Survival Outcome ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Online Databases ◽  
The Relationship

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

scholarly journals Development of an Immune-Related LncRNA Prognostic Signature for Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yudong Cao ◽  
Hecheng Zhu ◽  
Jun Tan ◽  
Wen Yin ◽  
Quanwei Zhou ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Clinical Outcome ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.

scholarly journals The Role of CXCL10 in Prognosis of Patients with Colon Cancer and Tumor Microenvironment Remodeling

2020 ◽  
Author(s):  
Hongli Yin ◽  
Weiwei Song ◽  
Chenguang Han ◽  
Qiantai Mao ◽  
Zhaoshuai Ji ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Cox Regression Analysis ◽  
Immune Cell Subsets ◽  
Intersection Analysis ◽  
Immune Score

Abstract Background: In the past few years, tumor microenvironment (TME) has gradually become a hot topic in tumor research, which has important significance in the diagnosis, prevention and prognosis of tumors. Importantly, the immune system is a major contributing factor in TME, and studies have shown that tumors are partially infiltrated with various immune cell subsets. The immune characteristics of the TME play an essential role in evaluating the prognosis of patients. The immune scoring system based on the distribution of tumor local immune cell subsets and cell density has been an essential indicator in the evaluation of patient prognosis and has been verified in various tumor studies. TME is indispensable in the occurrence and development of Colorectal cancer (CRC). However, understanding the dynamic regulation of immunity and matrix components in TME of CRC is still a challenge and should be investigated further.Methods: In this study, we collected transcriptome RNA-seq data of 521 Colon adenocarcinoma (COAD) patients from The Cancer Genome Atlas (TCGA) data portal. We then estimate the fraction of stromal and immune cells in COAD cases by ESTIMATE algorithms [1]. A total of 1109 stromal-immune score-related differentially expressed genes (DEGs) were identified and used to generate a high-confidence protein–protein intersection (PPI) network and univariate COX regression analysis. Intersection analysis of the data from PPI network and univariate COX regression analysis showed the core gene. Then we performed Gene set enrichment analysis (GSEA), survival analysis and clinical analysis for CXCL10, and applied CIBERSORT algorithms to estimate the tumor-infiltrating immune cells (TICs) proportion in COAD cases.Results: The proportion of immune and stromal components in TME are associated with the progression of COAD. For example, tumor metastasis is inversely proportional to immune score. A total of 1109 DEGs were obtained by analyzing the low-score shared genes and the high-score shared genes by intersection analysis which might be the determinant of TME status. The GO enrichment analysis indicated that DEGs are associated with immune-related terms. KEGG pathway enrichment analysis showed that these DEGs are mainly enriched in cytokine cytokine receptor signaling pathway etc. Therefore, DEGs are related to immune regulation, which indicates that the participation of immune factors is the main characteristic of TME in COAD. Moreover, the expression level of CXCL10 has significantly connection with the prognosis of patients and the progression of COAD. Conclusion: Taken together, we conducted a comprehensive analysis of the TME in COAD, and predicted a prognostic indicator for COAD, which provided a novel insight for therapeutics of COAD.

scholarly journals Network models of prostate cancer immune microenvironments identify ROMO1 as heterogeneity and prognostic marker

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Lei Wang ◽  
Xudong Liu ◽  
Zhe Liu ◽  
Yafan Wang ◽  
Mengdi Fan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Network Models ◽  
Principal Component ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
External Data ◽  
Key Genes ◽  
Tcga Dataset

AbstractProstate cancer (PCa) is the fifth leading cause of death from cancer in men worldwide. Its treatment remains challenging due to the heterogeneity of the tumor, mainly because of the lack of effective and targeted prognostic markers at the system biology level. First, the data were retrieved from TCGA dataset, and valid samples were obtained by consistent clustering and principal component analysis; next, key genes were analyzed for prognosis of PCa using WGCNA, MEGENA, and LASSO Cox regression model analysis, while key genes were screened based on disease-free survival significance. Finally, TIMER data were selected to explore the relationship between genes and tumor immune infiltration, and GSCAlite was used to explore the small-molecule targeted drugs that act with them. Here, we used tumor subtype analysis and an energetic co-expression network algorithm of WGCNA and MEGENA to identify a signal dominated by the ROMO1 to predict PCa prognosis. Cox regression analysis of ROMO1 was an independent influence, and the prognostic value of this biomarker was validated in the training set, the validated data itself, and external data, respectively. This biomarker correlates with tumor immune infiltration and has a high degree of infiltration, poor prognosis, and strong correlation with CD8+T cells. Gene function annotation and other analyses also implied a potential molecular mechanism for ROMO1. In conclusion, we putative ROMO1 as a portal key prognostic gene for the diagnosis and prognosis of PCa, which provides new insights into the diagnosis and treatment of PCa.

scholarly journals Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Huiling Wang ◽  
Shuo You ◽  
Meng Fang ◽  
Qian Fang
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Clustering Algorithm ◽  
Immune Cell ◽  
Cox Regression ◽  
Therapeutic Potential ◽  
Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Background. Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. Method. The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. Results. Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. Conclusion. We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.

scholarly journals TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Jiang ◽  
Dachang Liu ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Information ◽  
Lasso Regression ◽  
Osteosarcoma Cells ◽  
Screening Process ◽  
Cox Regression Analysis ◽  
Human Osteosarcoma Cells

IntroductionOsteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.Materials and MethodsThe gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.ResultsThe screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.ConclusionThe TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

scholarly journals Prognostic value and immune infiltration of novel signatures in colon cancer microenvironment

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yilin Lin ◽  
Xiaoxian Pan ◽  
Zhihua Chen ◽  
Suyong Lin ◽  
Zhanlong Shen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
Prognostic Value ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cell Content ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Abstract Background Growing evidence has shown that the prognosis for colon cancer depends on changes in microenvironment. The purpose of this study was to elucidate the prognostic value of long noncoding RNAs (lncRNAs) related to immune microenvironment (IM) in colon cancer. Methods Single sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of colon cancer based on the immune genomes of 29 immune signatures. Cox regression analysis identified a lncRNA signatures associated with immune infiltration. The Tumor Immune Estimation Resource database was used to analyze immune cell content. Results Colon cancer samples were divided into three subtypes by unsupervised cluster analysis. Cox regression analysis identified an immune infiltration-related 5-lncRNA signature. This signature combined with clinical factors can effectively improve the predictive ability for the overall survival (OS) of colon cancer. At the same time, we found that the expression of H19 affects the content of B cells and macrophages in the microenvironment of colon cancer and affects the prognosis of colon cancer. Finally, we constructed the H19 regulatory network and further analyzed the possible mechanisms. We found that knocking down the expression of H19 can significantly inhibit the expression of CCND1 and VEGFA. At the same time, the immunohistochemical assay found that the expression of CCND1 and VEGFA protein was significantly positively correlated with the infiltration of M2 type macrophages. Conclusion The findings may help to formulate clinical strategies and understand the underlying mechanisms of H19 regulation. H19 may be a biomarker for targeted treatment of colon cancer.

scholarly journals A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhijian Huang ◽  
Chen Xiao ◽  
Fushou Zhang ◽  
Zhifeng Zhou ◽  
Liang Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Comprehensive Analysis ◽  
Cancer Prognosis ◽  
Clinicopathological Factors ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC.Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed.Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells.Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.

scholarly journals Identification of a Costimulatory Molecule-Related Signature for Predicting Prognostic Risk in Prostate Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Shengdong Ge ◽  
Xiaoliang Hua ◽  
Juan Chen ◽  
Haibing Xiao ◽  
Li Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Costimulatory Molecule ◽  
Prognostic Significance ◽  
Gene Expression Profiles ◽  
Treatment Decision ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in prostate cancer (PCa) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in PCa and construct a prognostic signature to improve treatment decision making and clinical outcomes. Five prognosis-related costimulatory molecule genes (RELT, TNFRSF25, EDA2R, TNFSF18, and TNFSF10) were identified, and a prognostic signature was constructed based on these five genes. This signature was an independent prognostic factor according to multivariate Cox regression analysis; it could stratify PCa patients into two subgroups with different prognoses and was highly associated with clinical features. The prognostic significance of the signature was well validated in four different independent external datasets. Moreover, patients identified as high risk based on our prognostic signature exhibited a high mutation frequency, a high level of immune cell infiltration and an immunosuppressive microenvironment. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for PCa patients.

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

2021 ◽  
Author(s):  
Shaomei Tang ◽  
Xiaoliang Huang ◽  
Haixing Jiang ◽  
Shanyu Qin
Keyword(s):  
B Cells ◽  
Pancreatic Adenocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cell Components

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

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