Identification of the key genes and characterizations of Tumor Immune Microenvironment in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC)

Zhang, Lemeng; Chen, Jianhua; Cheng, Tianli; Yang, Hua; Li, Haitao; Pan, Changqie

doi:10.7150/jca.42531

Cited by 39 publications

(34 citation statements)

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“…The RYR3 gene encodes for a ryanodine receptor which mediates the calcium release for many cellular processes. Ryanodine receptor has been reported to be involved in epithelial-mesenchymal transition, in tumour cell apoptosis and treatment resistance in some cancers, including lung AC [46][47][48]. These data may provide the rationale for post-operative risk strati cation with a differential follow-up scheme.…”

Section: Discussionmentioning

confidence: 89%

Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

Kirienko

Sollini

Corbetta

et al. 2021

Preprint

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Objectives The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F]-FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n=74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalized linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.Results Standardized Uptake Value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule in predicting the outcome resulted in an Area Under the Curve (AUC) of 0.87.Conclusions: Radiogenomic data provided clinically relevant information in NCSCL patients, regarding the histotype, aggressiveness, and progression. Gene expression may provide additional valuable information to guide patient management. The application of ML allows to increase the efficacy of radiogenomic analysis and provide novel insights into cancer biology.

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Section: Discussionmentioning

confidence: 89%

Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

Kirienko

Sollini

Corbetta

et al. 2021

Preprint

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“…Research has indicated that the TIME was closely related with the prognosis of cancers, including hepatocellular carcinoma ( Hu Z.Q. et al, 2020 ), lung adenocarcinoma, lung squamous cell carcinoma ( Zhang L. et al, 2020 ), and so on. Therefore, it is of great benefit to identify the vital potential biomarkers associated with the remodeling of the TME and prognosis, and develop more specific drugs against CC.…”

Section: Discussionmentioning

confidence: 99%

TGFβ1: An Indicator for Tumor Immune Microenvironment of Colon Cancer From a Comprehensive Analysis of TCGA

Wang

Wang²,

et al. 2021

Front. Genet.

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BackgroundTumor microenvironment (TME) and tumor-infiltrating immune cells (TICs) greatly participate in the genesis and development of colon cancer (CC). However, there is little research exploring the dynamic modulation of TME.MethodsWe analyzed the proportion of immune/stromal component and TICs in the TME of 473 CC samples and 41 normal samples from The Cancer Genome Atlas (TCGA) database through ESTIMATE and CIBERSORT algorithms. Correlation analysis was conducted to evaluate the association between immune/stromal component in the TME and clinicopathological characteristics of CC patients. The difference analysis was performed to obtain the differentially expressed genes (DEGs). These DEGs were further analyzed by GO and KEGG enrichment analyses, PPI network, and COX regression analysis. Transforming growth factor β1 (TGFβ1) was finally overlapped from the above analysis. Paired analysis and GSEA were carried out to understand the role of TGFβ1 in colon cancer. The intersection between the difference analysis and correlation analysis was conducted to learn the association between TGFβ1 and TICs.ResultsOur results showed that the immune component in the TME was negatively related with the stages of CC. GO and KEGG enrichment analysis revealed that 1,110 DEGs obtained from the difference analysis were mainly enriched in immune-related activities. The intersection analysis between PPI network and COX regression analysis indicated that TGFβ1 was significantly associated with the communication of genes in the PPI network and the survival of CC patients. In addition, TGFβ1 was up-regulated in the tumor samples and significantly related with poor prognosis of CC patients. Further GSEA suggested that genes in the TGFβ1 up-regulated group were enriched in immune-related activities and the function of TGFβ1 might depend on the communications with TICs, including T cells CD4 naïve and T cells regulatory.ConclusionThe expression of TGFβ1 might be an indicator for the tumor immune microenvironment of CC and serve as a prognostic factor. Drugs targeting TGFβ1 might be a potential immunotherapy for CC patients in the future.

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“…In the last few years, TME was found to play a vital role in the initiation and progression of tumorigenesis [29][30][31][32] . Researches indicated that TIME was closely related with the prognosis of cancers, including hepatocellular carcinoma [33] , lung adenocarcinoma, lung squamous cell carcinoma [34] and so on. Therefore, it is of great bene t to identify the vital potential biomarkers associated with the remodeling of TME and prognosis, and develop more speci c drugs against CC.…”

Section: Discussionmentioning

confidence: 99%

TGFβ1: an Indicator for Tumor Immune Microenvironment (TIME) of Colon Cancer from a Comprehensive Analysis of TCGA

Wang

Wang²,

et al. 2020

Preprint

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Background: Tumor microenvironment (TME) and tumor-infiltrating immune cells (TIC) greatly participated in the genesis and development of colon cancer (CC). However, there are few researches exploring the dynamic modulation of TME. Methods: In our study, we analyzed the proportion of immune/stromal component and TIC in TME of 473 CC samples and 41 normal samples from The Cancer Genome Atlas (TCGA) database through ESTIMATE and CIBERSORT algorithm. Correlation analysis was carried out to evaluate the association between immune/stromal component in TME and clinicopathological characteristics of CC patients. The difference analysis was performed to obtain the differentially expressed genes (DEGs). These DEGs were further analyzed by gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses, protein–protein interaction (PPI) network construction and COX regression analysis. Transforming growth factor β1 (TGFβ1) was finally overlapping from the above analysis. Furthermore, TGFβ1 was analyzed by paired analysis, Gene Set Enrichment Analysis (GSEA). The intersection between the difference analysis and correlation analysis was also conducted to learn the association between TGFβ1 and TICs.Results: Our result showed that immune component in TME was negatively related with the stages of CC. GO and KEGG enrichment analysis revealed that 1110 DEGs obtained from difference analysis were mainly enriched in immune-related activities. The intersection analysis between PPI network and COX regression analysis indicted that TGFβ1 was significantly associated with the communication of genes in PPI network and the Hazard Ratio (HR) of CC patients’ survival. In addition, TGFβ1 was up-regulated in the tumor samples and significantly related with poor prognosis of CC patients. Further GSEA suggested that genes in TGFβ1 up-regulated group were primarily enriched in immune-related activities and the function of TGFβ1 might depend on the communications with TICs, including T cells CD4 naïve and T cells regulatory (Tregs). Conclusions: The expression of TGFβ1 might be an indicator for tumor immune microenvironment (TIME) of CC and sever as a prognostic factor of CC. Drugs targeting TGFβ1 might be a potential immunotherapy for CC patients in the future.

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Identification of the key genes and characterizations of Tumor Immune Microenvironment in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC)

Cited by 39 publications

References 50 publications

Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

TGFβ1: An Indicator for Tumor Immune Microenvironment of Colon Cancer From a Comprehensive Analysis of TCGA

TGFβ1: an Indicator for Tumor Immune Microenvironment (TIME) of Colon Cancer from a Comprehensive Analysis of TCGA

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