Dataset Information

A novel immune checkpoints-based signature to predict prognosis and response to immunotherapy in lung adenocarcinoma.

ABSTRACT:

Background

Except for B7-CD28 family members, more novel immune checkpoints are being discovered. They are closely associated with tumor immune microenvironment and regulate the function of many immune cells. Various cancer therapeutic studies targeting these novel immune checkpoints are currently in full swing. However, studies concerning novel immune checkpoints phenotypes and clinical significance in lung adenocarcinoma (LUAD) are still limited.

Methods

We enrolled 1883 LUAD cases from nine different cohorts. The samples from The Cancer Genome Atlas (TCGA) were used as a training set, whereas seven microarray data cohorts and an independent cohort with 102 qPCR data were used for validation. The immune profiles and potential mechanism of the system were also explored.

Results

After univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a novel immune checkpoints-based system (LTA, CD160, and CD40LG) were identified from the training set, which significantly stratified patients into high- and low-risk groups with different survivals. Furthermore, this system has been well validated in different clinical subgroups and multiple validation cohorts. It also acted as an independent prognostic factor for patients with LAUD in different cohorts. Further exploration suggested that high-risk patients exhibited distinctive immune cells infiltration and suffered an immunosuppressive state. Additionally, this system is closely linked to various classical immunotherapy biomarkers.

Conclusion

we constructed a novel immune checkpoints-based system for LUAD, which predicts prognosis and immunotherapeutic implications. We believe that these findings will not only aid in clinical management but will also shed some light on screening appropriate patients for immunotherapy.

SUBMITTER: Sun N

PROVIDER: S-EPMC9310422 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.