The paradoxical role of tumor-infiltrating immune cells in lung cancer

Zheng, Xiaodan; Hu, Yuhai; Yao, Chengfang

doi:10.5582/irdr.2017.01059

Cited by 37 publications

(29 citation statements)

References 81 publications

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“…We determined that the genes in the red and lightcyan module were enriched in the biological pathways involved in the regulation of immune response, complement activation, and altered T cells and B cells signalling. These key signalling pathways are closely related to the tumour microenvironment [74]. Similarly, Choi et al [75] identified through WGCNA magenta and brown module of co-expressed genes in lung adenocarcinoma which were significantly correlated with tumour metabolism and immune microenvironment.…”

Section: Discussionmentioning

confidence: 92%

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Niemira

Collin

Szałkowska

et al. 2019

Cancers

220

166

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Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

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Section: Discussionmentioning

confidence: 92%

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Niemira

Collin

Szałkowska

et al. 2019

Cancers

220

166

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“…Cancer is a highly heterogeneous disease involving complex interactions between the malignant cells and the tumor microenvironment (TME). The latter consists of various immune cells, mesenchymal-origin cells, and the extracellular matrix (ECM) ( 1 , 2 ), which influence all stages of tumorigenesis by directly interacting with the tumor cells ( 3 , 4 ). The immunological component of the TME acts as a two-edged sword that can either suppress or promote tumor development ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

“…Lung adenocarcinoma (LUAD) is the most common histological subtype ( 10 ). Studies show the infiltration of multiple immune cells in the lung TME ( 4 , 11 ), including that of T lymphocytes, B cells, dendritic cells (DCs), macrophages, and natural killer (NK) cells ( 12 ). In fact, the relative proportion of these tumor-infiltrating immune cells creates the microenvironment of lung cancer ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…As reported in the immunoediting theory, tumor invasive immune cells (TIICs) play a "double-edged sword" role in the development of cancers. A large number of studies have found the occurrence and development of lung adenocarcinoma not only depends on the lung cancer cells themselves but also is regulated by the tumor-in ltrating immune cells in the lung cancer microenvironment [32].…”

Section: Discussionmentioning

confidence: 99%

Construction of NEIL3 as a Prognostic Biomarker and Co-expressed Prognostic Signature in Lung Adenocarcinoma

Zhao

Liu

Zhou

et al. 2020

Preprint

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Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death. This study aimed to develop and validate reliable prognostic biomarkers and signature.Methods: Differentially expressed genes were identified based on three Gene Expression Omnibus (GEO) datasets. Based on three LUAD cohorts’ data included 1052 samples and extracted from our cohort, GEO, The Cancer Genome Atlas, we explored clinicopathological features and the expression of NEIL3 to determine its clinical effect in LUAD. Western blotting, Real-time quantitative PCR; (22 pairs of tumor and normal tissues), and immunohistochemical analyses (406-tumor tissues subjected to microarray) were conducted. TIMER and ImmuCellAI analyzed relationship between NEIL3 expression and the abundance of tumor-infiltrating immune cells in LUAD. The co-expressed-gene prognostic signature was established based on the Cox regression analysis.Results: This study identified 502 common differentially expressed genes and confirmed that NEIL3 was significantly overexpressed in LUAD samples(P<0.001). Increased NEIL3 expression was related to advanced stage, larger tumor size and poor overall survival (p < 0.001) in three LUAD cohorts. The proportions of natural T regulatory cells and induced T regulatory cells increased in the high NEIL3 group, whereas those of B cells, Th17 cells and dendritic cells decreased. Gene set enrichment analysis indicated that NEIL3 may activate cell cycle progression and P53 signaling pathway, leading to poor outcomes. We identified nine prognosis-associated hub genes among 370 genes co-expressed with NEIL3. A 10-gene prognostic signature including NEIL3 and nine key co-expressed genes was constructed. Higher risk-score was correlated with more advanced stage, larger tumor size and worse outcome (p<0.05). Finally, the signature was verified in test cohort (GSE50081) with superior diagnostic accuracy. Conclusions: This study suggested that NEIL3 has the potential to be an immune-related therapeutic target and an independent predictor for LUAD. We also developed a prognostic signature for LUAD with a precise diagnostic accuracy.

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The paradoxical role of tumor-infiltrating immune cells in lung cancer

Cited by 37 publications

References 81 publications

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

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

Construction of NEIL3 as a Prognostic Biomarker and Co-expressed Prognostic Signature in Lung Adenocarcinoma

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The paradoxical role of tumor-infiltrating immune cells in lung cancer

Cited by 37 publications

References 81 publications

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

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

Construction of NEIL3&nbsp;as a Prognostic Biomarker and Co-expressed Prognostic Signature in Lung Adenocarcinoma

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Construction of NEIL3 as a Prognostic Biomarker and Co-expressed Prognostic Signature in Lung Adenocarcinoma