Dataset Information

Construction of immune cell infiltration score model to assess prognostic ability of tumor immune environment in lung adenocarcinoma.

ABSTRACT:

Objectives

The immune cell infiltration (ICI) in the tumor microenvironment (TME) can provide a reference for prognosis after immunotherapy. We aim to establish an ICI scoring model and evaluate its predictive ability for the immunotherapy efficacy and the prognosis in lung adenocarcinoma (LUAD) patients.

Methods

We developed and analyzed the landscape of infiltrative immune cells based on the CIBERSORT and ESTIMATE algorithms. Then, three clusters of LUAD patients were discerned from TCGA-LUAD and GSE11969 data. Furthermore, two gene clusters were classified based on the PCA.

Results

LUAD patients with better prognoses tend to have higher immune checkpoint expression and immune/stromal scores. There is a correlation between TMB and ICI, and their relationship deserves further exploration. Moreover, the early-stage and male patients with high ICI scores have more prolonged survival.

Conclusions

The feasibility of the ICI score model in evaluating prognosis after immune checkpoint therapy for LUAD patients was verified, specifically reflected in the screening of sensitive immune checkpoints as a treatment reference. The scoring system can accurately predict the overall survival of LUAD patients, which has clinical value to monitor disease and evaluate prognosis.

SUBMITTER: Tian J

PROVIDER: S-EPMC10086903 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Construction of immune cell infiltration score model to assess prognostic ability of tumor immune environment in lung adenocarcinoma.

Tian Jiahui J Fu Chunyan C Peng Qian Q Yang Jialu J Fan Xiaoxiao X Zeng Xuan X Qing Weiguo W Wu Yi Y

American journal of translational research 20230315 3

<h4>Objectives</h4>The immune cell infiltration (ICI) in the tumor microenvironment (TME) can provide a reference for prognosis after immunotherapy. We aim to establish an ICI scoring model and evaluate its predictive ability for the immunotherapy efficacy and the prognosis in lung adenocarcinoma (LUAD) patients.<h4>Methods</h4>We developed and analyzed the landscape of infiltrative immune cells based on the CIBERSORT and ESTIMATE algorithms. Then, three clusters of LUAD patients were discerned ...[more]

PMID: 37056847

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Project description: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. 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.

Project description:Background: Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. Methods: First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. Results: In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (CLEC17A, INHA, and XIRP1) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs CLEC17A, INHA, and XIRP1 were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. Conclusions: The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.

Project description:The RTK/RAS pathway is an oncogenic signaling pathway for which many targeted drugs have been developed; however, survival remains poor. A combination of targeted therapy and immunotherapy has emerged as an option for improving cancer treatment responses. In this study, on the basis of the expression, survival, single nucleotide variation (SNV), copy number variation (CNV), and methylation data of lung adenocarcinoma (LUAD) from The Cancer Genome Atlas database, we comprehensively analyzed the genomic changes in the RTK/RAS pathway and their associations with tumor-infiltrating lymphocytes (TIL) and prognosis in LUAD to provide the genomics landscape of RTK/RAS with TIL and prognosis. We found that two rarely mutated genes, mitogen-activated protein kinase kinase 1 and insulin-like growth factor 1 receptor, were significantly associated with the worse survival of patients with LUAD. Patients with LUAD and co-mutation of KRAS proto-oncogene (KRAS) and neurofibromin 1 genes had worse survival, and the underlying mechanism could be insufficient for protein synthesis and intracellular signal deactivation. Methylation of the Rac family small GTPase 1 (RAC1) was associated with better survival. The SNVs of the top mutated genes, including epidermal growth factor receptor (12.7%), neurotrophic receptor tyrosine kinase 3 (7.8%), erb-b2 receptor tyrosine kinase 4 (8.5%), and KRAS (29.6%), were associated with T cell exhaustion in LUAD. To construct nomograms, we further screened the genes whose genomic changes were closely associated with survival and immune infiltration. The nomograms performed well in predicting disease-specific survival (DSS) with a concordance index of 0.7 (0.589, 0.811) and overall survival with a concordance index of 0.689 (0.603, 0.775) in test set; they also showed good correspondence between actual and ideal nomogram predictions. Tumor stage, RAC1 methylation, and type 1 regulatory T cells greatly contributed to DSS and OS nomograms. In summary, we provided a comprehensive genomic profile of the RTK/RAS pathway in LUAD and its association with immune cell infiltration and prognosis of LUAD. This profile would serve as a basis for developing better therapeutic strategies, improving patient prognosis, and understanding the mechanisms of immune disturbance from the perspective of oncogenic pathways of LUAD.

Project description:BackgroundTUBA1C is a microtubule component that is involved in a variety of cancers. Our main objective was to investigate TUBA1C expression, its prognostic value, its potential biological functions, and its impact on the immune system of patients with lung adenocarcinoma (LUAD).MethodsThe Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA) and Immunohistochemistry Analysis were used to analyze TUBA1C expression, its clinicopathology, overall survival (OS), and disease-free survival (DFS) in LUAD patients. We also determined the correlation between TUBA1C and tumor-infiltrating immune cells (TIICs) by using CIBERSORT and GEPIA databases. To determine the expression of TUBA1C in LUAD, we analyzed a collection of immune infiltration levels and cumulative survival of LUAD tissues in TIMER database. By using UALCAN, STRING, and GeneMANIA databases, we investigated the protein-coding genes related to TUBA1C and its co-expression genes in LUAD tissues. Gene set enrichment analysis (GSEA) was performed by using the TCGA dataset.ResultsThe mRNA and the protein expression of TUBA1C were found to be up-regulated in LUAD tissues. The univariate analysis indicated that an increased expression of TUBA1C was significantly correlated to the following parameters: age, stage, and lymph node metastasis. An over-expression of TUBA1C was associated with a poor prognosis of LUAD. In TIMER and CIBERSORT databases, we found that TUBA1C is correlated with 13 types of TIICs: activated B cell, activated CD4 T cell, central memory CD4 T cell, effector memory CD8 T cell, eosinophils, immature B cell, gamma-delta T cell, immature dendritic cell, mast cell, memory B cell, natural killer T cell, regulatory T cell, and type 2T helper cell. By performing GSEA, we found that TUBA1C is closely correlated to cell cycle, p53 signaling pathway, glycolysis, and gluconeogenesis.ConclusionsOur findings indicate that TUBA1C is associated with TIICs in tumor microenvironment. Therefore, it serves as a novel prognostic biomarker and a target for future treatment methods of LUAD.

Dataset Information

Construction of immune cell infiltration score model to assess prognostic ability of tumor immune environment in lung adenocarcinoma.

Objectives

Methods

Results

Conclusions

Publications

Construction of immune cell infiltration score model to assess prognostic ability of tumor immune environment in lung adenocarcinoma.

Similar Datasets

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