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. 2020 Jun 30:11:1218.
doi: 10.3389/fimmu.2020.01218. eCollection 2020.

VSports注册入口 - Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma

Shuguang Zuo  1 , Min Wei  1 , Shiqun Wang  1 , Jie Dong  1 , Jiwu Wei  1   2
Affiliations

Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma

Shuguang Zuo et al. Front Immunol. .

Abstract

Background: The tumor microenvironment (TME) consists of heterogeneous cell populations, including malignant cells and nonmalignant cells that support tumor proliferation, invasion, and metastasis through extensive cross talk. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy. Methods: Gene expression data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Immune cell infiltration was determined by single-sample Gene Set Enrichment Analysis (ssGSEA) depending on the integrated immune gene sets from published studies. Univariate analysis was used to determine the prognostic value of the infiltrated immune cells. Least absolute shrinkage and selection operator (LASSO) regression was performed to screen for the most survival-relevant immune cells VSports手机版. An immune-cell characteristic score (ICCS) model was constructed by using multivariate Cox regression analysis. Results: The immune cell infiltration patterns across 32 cancer types were identified, and patients in the high immune cell infiltration cluster had worse overall survival (OS) but better progression-free interval (PFI) compared to the low immune cell infiltration cluster. However, immune cell infiltration showed inconsistent prognostic value depending on the cancer type. High immune cell infiltration (High CI) indicated a worse prognosis in brain lower grade glioma (LGG), glioblastoma multiforme (GBM), and uveal melanoma (UVM), and favorable prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), sarcoma (SARC), and skin cutaneous melanoma (SKCM). LUAD prognosis was significantly influenced by the infiltration of 13 immune cell types, with high infiltration of all but Type 2 T helper (Th2) cells correlating with a favorable prognosis. The ICCS model based on six most survival-relevant immune cell populations was generated that classified patients into low- and high-ICCS groups with good and poor prognoses, respectively. The multivariate and stratified analyses further revealed that the ICCS was an independent prognostic factor for LUAD. Conclusions: The infiltration of immune cells in 32 cancer types was quantified, and considerable heterogeneity was observed in the prognostic relevance of these cells in different cancer types. An ICCS model was constructed for LUAD with competent prognostic performance, which can further deepen our understanding of the TME of LUAD and can have implications for immunotherapy. .

Keywords: immune cell infiltration; lung adenocarcinoma; prognosis; single-sample Gene Set Enrichment Analysis; tumor microenvironment. V体育安卓版.

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"VSports在线直播" Figures

Figure 1
Figure 1
Correlation of the immune cells across the pan-cancer cohort. After calculating the single-sample Gene Set Enrichment Analysis (ssGSEA) score representing the immune cells in the pan-cancer cohort (9,112 patients), a Pearson's correlation analysis between each immune cell was performed. The result was visualized using the R package “corrplot.” The size of the sector area and the gradient of colors represented the correlation coefficient R. “ × ” means no statistical significance (P > 0.05).
Figure 2
Figure 2
Correlation between the immune cell infiltration and survival in the pan-cancer cohort. (A) Unsupervised clustering separates The Cancer Genome Atlas (TCGA) pan-cancer cohort of 9,112 patients into two distinct immunophenotypes using the single-sample Gene Set Enrichment Analysis (ssGSEA) scores which represent the 46-cell infiltration. “Red color cluster” represents “hot” tumors with more immune cell infiltration, “blue color cluster” represents “cold” tumors with less immune cell infiltration. (B) Kaplan–Meier curves estimate the survival differences between the high cell infiltration cluster and the low cell infiltration cluster. Survival differences between the two clusters were detected by both Cox regression and log-rank methods. OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval; DFI, disease-free interval.
Figure 3
Figure 3
The prognosis value of the infiltrating immune cells in each cancer type. The association between the infiltrating immune cells and the survival of tumor patients was investigated using Cox regression and log-rank methods. The hazard ratio (HR) is <1.0, indicating a good effect on prognosis, and the HR value is greater than 1.0, indicating an adverse effect on prognosis. Cox P is represented by the size of points, and log-rank P is represented by the gradient of colors. P < 0.05 was used as the cutoff for significance. OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval; DFI, disease-free interval.
Figure 4
Figure 4
Kaplan–Meier curves of the 13 infiltrating immune cells in LUAD. The association between the infiltrating immune cells and the overall survival of LUAD patients was investigated using Cox regression and log-rank methods. Kaplan–Meier curves was drawn by the “survival” package based on R.
Figure 5
Figure 5
Identification of the immune-cell characteristic score (ICCS) and investigation of its prognostic value in lung adenocarcinoma (LUAD). (A) Cross-validation for tuning parameter selection in the least absolute shrinkage and selection operator (LASSO) model. (B) LASSO coefficient profiles of 13 prognosis-related immune cell populations. Variables whose LASSO coefficient is not equal to zero were used as candidate variables to construct the ICCS model. (C) The ICCS model classifies patients into low-ICCS and high-ICCS groups. (D) Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves of the prognostic ICCS model in the training set [The Cancer Genome Atlas (TCGA)]. The association between the ICCS and the survival of patients was investigated using Cox regression and log-rank methods. (E) Kaplan–Meier curves and time-dependent ROC curves of the prognostic ICCS model in the validating set [three Gene Expression Omnibus (GEO) datasets].
Figure 6
Figure 6
Stratification analysis on The Cancer Genome Atlas (TCGA) cohort based on immune-cell characteristic score (ICCS) and pathologic stage. (A) Kaplan–Meier analysis and time-dependent receiver operating characteristic (ROC) curves show the prognostic values of the pathologic stage using the TCGA cohort. (B) Kaplan–Meier analysis and time-dependent ROC curves present the prognostic values for patients grouped by combining the stage and the ICCS.
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