A comprehensive analysis of potentially targetable genetic aberrations and clinical findings in 821 patients with squamous-cell NSCLC – a comparison of NGM and TCGA LUSC data

Koleczko, Sophia; Schäpers, C.; Scheffler, Matthias; Ihle, Michaela A.; Kostenko, Anna; Michels, Sebastian; Fischer, Rudolf‐Josef; Nogová, Lucia; Brandes, Vanessa; Abdulla, Diana S.Y.; Ueckeroth, Frank; Thurat, M.; Frank, Rieke; Eisert, Anna; Bitter, Elisabeth; Wömpner, Claudia; Gogl, Leonie; Merkelbach‐Bruse, Sabine; Büttner, Reinhard; Wolf, Juergen

doi:10.1093/annonc/mdw383.13

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“…A publicly accessible gene expression database The Cancer Genome Atlas (TCGA) allows for the discovery and categorization of genomic abnormalities in large-scale datasets from around the globe. By using this approach, the potential associations exist between genes and prognostic outcomes in LUSC, can be explored (15)(16)(17). In this work, we downloaded the TCGA LUSC dataset and determined the immune and stromal score of patients using the ESTIMATE algorithm.…”

Section: Introductionmentioning

confidence: 99%

An investigation to identify tumor microenvironment-related genes of prognostic value in lung squamous cell carcinoma based on The Cancer Genome Atlas

Liu¹,

Liu²,

Zhang³

et al. 2021

Transl Cancer Res TCR

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Background: Lung squamous cell carcinoma (LUSC) is a prevalent and lethal malignancy with a poor clinical prognosis. Major constituents of the tumor microenvironment (TME) include infiltrating immune cells and stromal cells, which play a pivotal role in the progression and growth of the disease. To improve the understanding of the prognostic influence of immune and stromal cell-related genes for patients with the disease, we performed a comprehensive bioinformatics analysis to identify TME-relevant biomarkers, and investigated the potential role of these candidate signatures in LUSC.Methods: Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) assessed the samples of LUSC obtained from The Cancer Genome Atlas (TCGA). The samples were grouped according to their immune/stromal scores (high or low). Multivariate cox regression and receiver operating characteristic curves (ROC) were implemented to construct the risk assessment model for prognosis prediction. The co-upregulated differentially expressed genes (DEGs) in the immune and stromal groups were used for further analyses. Overall survival (OS) curves were used to determine the prognostic value of the DEGs, and the TME-related DEGs were verified with Gene Expression Omnibus (GEO) datasets. The functional assessments were performed include Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analyses. Results:The immune/stromal scores calculated by ESTIMATE showed significant associations with OS (log-rank P<0.05). In addition, the prognostic risk score model based on immune and stromal scores also showed significant correlations with OS. A total of 94 TME-related genes were obviously related to poor OS. Among them, BHMT2, FES, HSPB7, NOVA2, LPAP2, and SEMA3B (BFHNLS) were confirmed using GSE4573 and GSE17710 datasets. The functional assessments exhibited those TME-related genes mostly participate in immune response, cytokine-cytokine receptor interaction, and metabolic pathways, which elucidated the probable correlation of TME with tumorigenesis in LUSC. Conclusions:In this study, 6 potential biomarkers named BFHNLS were identified as TME-related genes with prognostic value based on immune and stromal scores of LUSC patients of TCGA, and were verified using GEO datasets, which might serve as therapeutic targets.

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Section: Introductionmentioning

confidence: 99%