Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer

Lu, Guangrong; Chen, Liping; Wu, Shengjie; Feng, Yuao; Lin, Tiesu

doi:10.1155/2020/8974793

Cited by 16 publications

(18 citation statements)

References 66 publications

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“…Several other studies have been published on immune-related prognostic signatures in EC ( 12 , 44 – 47 ). However, the present study has some uniqueness and advantages.…”

Section: Discussionmentioning

confidence: 99%

“…Third, the present study enrolled two independent ESCC data sets; the GEO data set included 179 samples from Chinese patients, and the TCGA data set included 95 samples from multinational patients, representing a more diverse genetic background. Finally, the number of samples included in this study is the largest among the available studies ( 12 , 44 – 47 ). The robustness of the results in this study was independently proven in both the training and validation sets, ensuring the high reliability of our results.…”

Section: Discussionmentioning

confidence: 99%

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An Immune Cell-Based Signature Associating With EMT Phenotype Predicts Postoperative Overall Survival of ESCC

Yang

et al. 2021

Front. Oncol.

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Esophageal squamous cell carcinoma (ESCC) is one of the deadliest solid malignancies and has a poor survival rate worldwide. In this study, we aimed to establish a tumor-infiltrating immune cell-based prognosis signature (IPS) to predict patients’ survival times and aid in the development of targeted therapies or immunotherapies. The abundances of 22 types of immune cells were determined by the CIBERSORT algorithm from ESCC patient gene expression data in the Gene Expression Omnibus (GEO) training set (n = 179) and The Cancer Genome Atlas (TCGA) validation set (n = 95). Then, the IPS was established by using the least absolute shrinkage and selection operator (LASSO) regression method. Kaplan-Meier analysis showed that patients with high IPS scores had significantly worse overall survival times than patients with low IPS scores in both the training set and the validation set (log-rank p = 0.001, and p = 0.050, respectively). Univariate and multivariate Cox regression analyses proved that the IPS was a robust prognostic factor for ESCC, independent of age, sex, tumor node metastasis (TNM) stage, pathology grade, and tumor location. In the mechanistic study, the epithelial-mesenchymal transition (EMT) process was identified by both gene set enrichment analysis (GSEA) and weighted correlation network analysis (WGCNA) as the underlying mechanism by which the IPS affects the prognosis of ESCC. After systematic correlation analyses, we found that M2 macrophages were the only cell type in the IPS significantly correlated with the EMT process. This relationship between M2 macrophage infiltration and the EMT phenotype was also confirmed by our preliminary immunochemistry (IHC) and multiplexed immunofluorescence study. In conclusion, we constructed an IPS that predicts the postoperative prognosis of ESCC patients and uncovered the critical role of M2 macrophages in the interplay between immune status and the EMT phenotype in ESCC.

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“…Several other studies have been published on immune-related prognostic signatures in EC ( 12 , 44 – 47 ). However, the present study has some uniqueness and advantages.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An Immune Cell-Based Signature Associating With EMT Phenotype Predicts Postoperative Overall Survival of ESCC

Yang

et al. 2021

Front. Oncol.

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“…LASSO is a statistical method that imposes L1 or L2 penalties to improve prediction accuracy and model interpretation, thereby generating sparse models [25]. In recent studies [26][27][28], LASSO regression has been used to integrate a single biomarker into a comprehensive analysis of multiple markers, and this method has been gradually developed and validated. To compare the feature screening effect of lasso regression, we also performed a full model incorporating all features.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

Cai

Shen

et al. 2020

Preprint

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Background: To develop and validate machine learning models for risk prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) using clinicopathologic parameters and immunohistochemical features. Methods: From January 2010 to December 2019, 639 patients' data were continuously collected in Nanfang Hospital. We exacted immunohistochemical features and clinicopathological features from the electronic medical records of patients. We established two models (a full model and a selection model) and implemented three algorithms (random forest, support vector machine and penalized logistic regression). The model performance was evaluated in terms of discrimination (receiver operating characteristic curve (AUC)), calibration, and decision curve analysis. Results: AUROC (area under receiver operating characteristic curve) analysis (also calibration curves) showed that the selection model (AUC values for training and testing, 0.843 and 0.840 respectively) and the full model constructed using random forest (AUC values for training and testing, 0.855 and 0.863 respectively) performed best among all models. Decision curve analysis depicted that the full model and the selection model using random forest was clinically useful. The model performance of the full model and the selection model were comparable. Conclusion: The random forest model using clinicopathologic- immunohistochemical features can predict the LNM of NSCLC patients.

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“…Yang et al established a prognostic immune signature for CSCC based on differential expression analysis and LASSO penalized Cox proportional hazards regression (Yang et al 2019). However, like many other immune-related models (Cheng et al 2016;Li et al 2020;Lu et al 2020), the variables were screened out based on single gene expression or immune cell proportion. Therefore, despite the PeerJ reviewing PDF | (2020:02:45770:1:1:NEW 1 Jul 2020)…”

Section: Manuscript To Be Reviewedmentioning

confidence: 99%

Peer Review #1 of "Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment (v0.1)"

2020

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The aim of this study was to explore the effective immune scoring method and mine the novel and potential immune microenvironment-related diagnostic and prognostic markers for cervical squamous cell carcinoma (CSSC). Materials and Methods: The Cancer Genome Atlas (TCGA) data was downloaded and multiple data analysis approaches were initially used to search for the immune-related scoring system on the basis of Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) algorithm. After-wards, the representative genes in the gene modules correlated with immune-related scores based on ESTIMATE algorithm were further screened using Weighted Gene Co-expression Network Analysis (WGCNA) and network topology analysis. Gene functions were mined through enrichment analysis, followed by exploration of the correlation between these genes and immune checkpoint genes. Finally, survival analysis was applied to search for genes with significant association with overall survival and external database was employed for further validation. Results: The immune-related scores based on ESTIMATE algorithm was closely associated with other categories of scores, the HPV infection status, prognosis and the mutation levels of multiple CSCC-related genes (HLA and TP53). 18 new representative immune microenvironment-related genes were finally screened closely associated with patient prognosis and were further validated by the independent dataset GSE44001. Conclusion: Our present study suggested that the immune-related scores based on ESTIMATE algorithm can help to screen out novel immune-related diagnostic indicators, therapeutic targets and prognostic predictors in CSCC.

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Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer

Cited by 16 publications

References 66 publications

An Immune Cell-Based Signature Associating With EMT Phenotype Predicts Postoperative Overall Survival of ESCC

An Immune Cell-Based Signature Associating With EMT Phenotype Predicts Postoperative Overall Survival of ESCC

Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

Peer Review #1 of "Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment (v0.1)"

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