A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer

Li, Jia; Li, Xin; Zhang, Chenyue; Zhang, Chenxing

doi:10.3892/or.2020.7464

Cited by 50 publications

(40 citation statements)

References 58 publications

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“…Immune score, which can promote the quantification of immune components (such as immune cells) in tumors, can significantly affect the prognosis of patients. In several studies (32)(33)(34)(35), it has been confirmed that high immune scores were associated with better prognosis. Similarly, high-risk patients with worse prognosis had lower immune scores in our study, which further suggested the validity and accuracy of the signature constructed in this study in identifying high-risk patients.…”

Section: Discussionmentioning

confidence: 87%

A Prognostic Nomogram Combining Immune-Related Gene Signature and Clinical Factors Predicts Survival in Patients With Lung Adenocarcinoma

Song

Guo

et al. 2020

Front. Oncol.

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The existence of tumor heterogeneity and complex carcinogenic mechanisms in lung adenocarcinoma (LUAD) make the most commonly used TNM staging system unable to well-interpret the prognosis of patients. Using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA) database, we constructed an immune signature based on a multivariate Cox analysis (stepwise model). We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients according to the pRRophetic algorithm. Gene-set variation analysis (GSVA) was used to reveal pathway enrichment between groups. Moreover, immune microenvironment landscape was described by single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT and systematically correlated with genomic of these patients. A prognostic nomogram combining the immune signature and TNM stage to predict the prognosis was developed by multivariate Cox regression. The novel signature with four immune-related genes (MAL, MS4A1, OAS1, and WFDC2) had good robustness, which can accurately distinguish between high-and low-risk patients. Compared with low-risk patients, high-risk patients with a worse prognosis (5-year OS: 46.5 vs. 59.4%, p = 0.002) could benefit more from immunotherapy and the application of common chemotherapeutic agents such as cisplatin and paclitaxel (Wilcoxon test, all p < 0.05). There were significant differences in tumor immune microenvironment and metabolic pathways between the two groups. Additionally, the constructed nomogram had reliable predictive performance with the C-index of 0.725 (95% CI = 0.668-0.781) in the development set (n = 500), 0.793 (95% CI = 0.728-0.858) in the internal validation set (n = 250) and 0.679 (95% CI = 0.644-0.714) in the external validation set (n = 442). The corresponding calibration curves also showed good consistency. To sum up, we developed an immune-related gene signature and comprehensively evaluated LUAD immune landscape and metabolic pathways. Effective differentiation of high-and low-risk patients and accurate construction of nomogram would be helpful to the development of individualized treatment strategies.

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Section: Discussionmentioning

confidence: 87%

A Prognostic Nomogram Combining Immune-Related Gene Signature and Clinical Factors Predicts Survival in Patients With Lung Adenocarcinoma

Song

Guo

et al. 2020

Front. Oncol.

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“…Ultimately, they screened 10 genes out of 448 DEGs that were constructed as the risk prediction model. The ten-gene model was more sensitive to prognosis than TNM staging [16]. However, few researches have been carried out on the correlation between tumor purity and development of LUAD.…”

Section: Discussionmentioning

confidence: 99%

Establishment and Evaluation of a 6-Gene Survival Risk Assessment Model Related to Lung Adenocarcinoma Microenvironment

Wang

Zhu

et al. 2020

BioMed Research International

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Objective. A survival risk assessment model associated with a lung adenocarcinoma (LUAD) microenvironment was established and evaluated to identify effective independent prognostic factors for LUAD. Methods. The public data were downloaded from the TCGA database, and ESTIMATE prediction software was used to score immune cells and stromal cells for tumor purity prediction. The samples were divided into the high-score group and the low-score group by the median value of the immune score (or stromal score). The Wilcoxon test was used for differential analysis. GO and KEGG enrichment analysis of differentially expressed genes (DEGs) was performed using “clusterProfiler” of R package. Meanwhile, univariate and multivariate regression analysis was performed on DEGs to construct a multivariate Cox risk regression model with variable gene expression levels as independent prognostic factors affecting a tumor microenvironment (TME) and tumor immunity. Results. This study found that LUAD patients with high immune cell (stromal cell) infiltration had better prognosis and were in earlier staging. Functional enrichment analysis revealed that most DEGs were related to the proliferation and activation of immune cells or stromal cells. A survival prediction model composed of 6 TME-related genes (CLEC17A, TAGAP, ABCC8, BCAN, FLT3, and CCR2) was established, and finally, the 6 feature genes closely related to the prognosis of LUAD were proved. The AUC value of the ROC curve in this model was 0.7, indicating that the model was reliable. Conclusion. Six genes related to the LUAD microenvironment have a predictive prognostic value in LUAD.

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“…High TMB indicates the presence of more neoantigens in the tumor microenvironment, which promote the in ammatory response and result in the alteration of transcriptomic and epigenetic signature [47]. It has been proved that gene expression signatures in the tumor microenvironment were associated with the prognosis in NSCLC [48][49][50][51]. In agreement with previous studies, the differentially expressed genes between TMB-H and TMB-L patients identi ed in this study were found to enrich in the immune-related damaged DNA binding, nuclear division, nuclear chromosome segregation, organelle ssion, single-stranded DNA binding, ribonucleoprotein complex binding and pyrimidine metabolism (Additional le 2: Figure S2) [52][53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Integrative modeling of multi-omics data for predicting tumor mutation burden in lung cancer patients

Chen

Wang

et al. 2020

Preprint

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Background Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarker for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. Methods To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts, and categorized into TMB-high (TMB-H) and TMB-low (TMB-L) groups respectively. Results Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs and 6 CpG sites. The multi-omics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multi-omics model score and TMB was observed. Conclusions In summary, we developed a prognostic TMB prediction model by integrating multi-omics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction (qRT-PCR) based TMB detection assay.

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A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer

Cited by 50 publications

References 58 publications

A Prognostic Nomogram Combining Immune-Related Gene Signature and Clinical Factors Predicts Survival in Patients With Lung Adenocarcinoma

A Prognostic Nomogram Combining Immune-Related Gene Signature and Clinical Factors Predicts Survival in Patients With Lung Adenocarcinoma

Establishment and Evaluation of a 6-Gene Survival Risk Assessment Model Related to Lung Adenocarcinoma Microenvironment

Integrative modeling of multi-omics data for predicting tumor mutation burden in lung cancer patients

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