Tumor-Infiltrating Immune Cell Signature Predicts the Prognosis and Chemosensitivity of Patients With Pancreatic Ductal Adenocarcinoma

Gao, Yinghong; Chen, Shipeng; Vafaei, Somayeh; Zhong, Xiaoli

doi:10.3389/fonc.2020.557638

Cited by 27 publications

(14 citation statements)

References 35 publications

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“…Based on the ImmPort Database 1 and the previous studies ( Li et al, 2017 ; Gao et al, 2020 ; Sun et al, 2020 ), a total of 3,265 immune-related genes (IRGs) were collected. Then, after the deletion of low-expression genes (FPKM < 0.5), these IRGs were included into the Lasso analysis to integrate multiple variables.…”

Section: Methodsmentioning

confidence: 99%

Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer

Dai

Liu

Liu³

et al. 2021

Front. Cell Dev. Biol.

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Background: Tumor immune microenvironment plays a vital role in tumorigenesis and progression of gastric cancer (GC), but potent immune biomarkers for predicting the prognosis have not been identified yet.Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses. Furthermore, the risk score of each sample was calculated, and GC patients were divided into high-risk group and low-risk group based on their risk scores. Subsequently, the performance of this signature, including the correlation with overall survival (OS), clinical features, immune cell infiltration, and immune response, has been tested in GC data from TCGA database and Gene Expression Omnibus (GSE84437), respectively.Results: An immune signature composed of four genes (MAGED1, ACKR3, FZD2, and CTLA4) was constructed. The single sample gene set enrichment analysis (ssGSEA) indicated that activated CD4+/CD8+ T cell, activated dendritic cell, and effector memory CD8+ T cell prominently increased in the low-risk group, showing relatively high immune scores and low stromal scores. Further GSEA analysis indicated that TGF-β, Ras, and Rap1 pathways were activated in the high-risk group, while Th17/Th1/Th2 differentiation, T cell receptor and PD-1/PD-L1 checkpoint pathways were activated in the low-risk group. Low-risk patients presented higher tumor mutation burden (TMB) and expression of HLA-related genes. The immune-associated signature showed an excellent predictive ability for 2-, 3-, and 5-year OS in GC.Conclusion: The immune-related prognosis model contributes to predicting the prognosis of GC patients and providing valuable information about their response to immunotherapy using integrated bioinformatics methods.

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Section: Methodsmentioning

confidence: 99%

Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer

Dai

Liu

Liu³

et al. 2021

Front. Cell Dev. Biol.

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“…Current research reveals that lncRNA plays an important role in the genesis and development of tumors through interactions with competing endogenous RNA (ceRNA) or by acting as microRNA sponges ( Tay et al, 2014 ). Tumor-infiltrating immune cells are a major component of the tumor microenvironment and affect the clinical outcomes and pathological staging of multiple tumor types ( Gao et al, 2020 ; Hong et al, 2020 ). Some studies suggest lncRNA can affect tumor development and tumor immune cell microenvironment through the ceRNA network ( Huang et al, 2018 ; Cao et al, 2019 ; Xu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Deng

Wang

et al. 2021

Front. Genet.

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Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient’s quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts.” Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

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“…Current research reveals that lncRNA plays an important role in the genesis and development of tumors through interactions with competing endogenous RNA (ceRNA) or by acting as microRNA sponges [7]. Tumor-in ltrating immune cells are a major component of the tumor microenvironment and affect the clinical outcomes and pathological staging of multiple tumor types [8,9]. Some studies suggest lncRNA can affect tumor development and tumor immune cell microenvironment through the ceRNA network [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Deng

Wang

et al. 2021

Preprint

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Purpose Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patients’ quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods We constructed a ceRNA network associated with KIRC by analyzing the long noncoding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from the Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “CIBERSORT”. Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism. Results We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [Area Under Curves (AUCs) of 1, 3 and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747 and 0.772; AUCs of 1, 3 and 5-year survivals in nomogram based on immune cells: 0.603, 0.642 and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusions Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

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Tumor-Infiltrating Immune Cell Signature Predicts the Prognosis and Chemosensitivity of Patients With Pancreatic Ductal Adenocarcinoma

Cited by 27 publications

References 35 publications

Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer

Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

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