Dataset Information

Integrated Analysis of Transcriptome and Prognosis Data Identifies FGF22 as a Prognostic Marker of Lung Adenocarcinoma.

ABSTRACT: Lung adenocarcinoma is one of the most common cancers worldwide. However, the molecular mechanisms of lung adenocarcinoma development are still unclear. This study aimed to investigate the expression profiles of anti-lung cancer target genes in different cancer stages and to explore their functions in tumor development. Lung adenocarcinoma transcriptome and clinical data were downloaded from Genomic Data Commons Data Portal, and the anti-lung cancer target genes were retrieved from the Thomson Reuters Integrity database. The results showed that 16 anti-lung target genes were deregulated in all stages. Among these target genes, fibroblast growth factor 22 showed the most important role in transcription regulatory networks. Further analysis revealed that APC, BRIP1, and PTTG1 may regulate fibroblast growth factor 22 and subsequently influence MAPK signaling pathway, Rap1 signaling pathways, and other tumorigenic processes in all stages. Moreover, high fibroblast growth factor 22 expression leads to poor overall survival (hazard ratio = 1.55, P = .019). These findings provide valuable information for the pathological research and treatment of lung adenocarcinoma. Future studies are needed to verify these results.

SUBMITTER: Liu HY

PROVIDER: S-EPMC6373997 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Integrated Analysis of Transcriptome and Prognosis Data Identifies FGF22 as a Prognostic Marker of Lung Adenocarcinoma.

Liu Hong-Yan HY Zhao Hui H Li Wen-Xing WX

Technology in cancer research & treatment 20190101

Lung adenocarcinoma is one of the most common cancers worldwide. However, the molecular mechanisms of lung adenocarcinoma development are still unclear. This study aimed to investigate the expression profiles of anti-lung cancer target genes in different cancer stages and to explore their functions in tumor development. Lung adenocarcinoma transcriptome and clinical data were downloaded from Genomic Data Commons Data Portal, and the anti-lung cancer target genes were retrieved from the Thomson R ...[more]

PMID: 30803369

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Project description:Background Lung adenocarcinoma (LUAD) accounts for the largest proportion of lung cancer patients and has the highest morbidity and mortality worldwide. Accumulating evidence shows that immune-associated long non-coding RNAs (lncRNAs) play a role in LUAD, although their predictive value for immunotherapy treatment and cancer-related death remains poorly investigated. Methods Gene expression profiles and clinical data were obtained from The Cancer Genome Atlas. We constructed a risk model by univariate and multivariate Cox regression and least absolute shrinkage and selection operator regression analysis and subsequently divided each sample into low- or high-risk category. Survival and receiver operating characteristic (ROC) analyses were applied to assess the prognostic value of the model. Additionally, immune and somatic mutation status were analysed between the two risk groups. Finally, the model was applied to pancreatic ductal adenocarcinoma (PDAC) samples to explore the applicability of the model in other cancers. Results We obtained data from 499 LUAD patients and randomised the samples into a training set (N=351) and validation set (N=148) at a 7:3 ratio. We detected 7 immune-associated lncRNAs (AP000695.2, AC026355.2, LINC01843, ITGB1-DT, LINC01150, AL590226.1 and AC091185.1) that were applicable for establishing a risk signature. Survival analysis revealed that patients categorised in the high-risk group had shorter overall survival (OS) than those in the low-risk group. ROC analyses showed excellent AUC values in all data sets (>0.65 at 1, 3, and 5 years). Notably, ESTIMATE algorithm and analysis of PCA, (ss)GSEA, and somatic mutations revealed that the high-risk group had a stronger immunosuppressive status and a higher tumour mutation burden (TMB). Moreover, patients in the low-risk group responded better to immunotherapy due to higher levels of immune-checkpoint receptor genes and TLS-related genes. Our model using the 7 immune-associated lncRNAs showed similar applicability for PDAC patients. Conclusions We constructed a model for risk signatures based on 7 immune-associated lncRNAs and showed its prognostic value for identifying immune and somatic mutation characteristics in LUAD patients, which may assist clinical treatment plans and elucidate molecular mechanisms of LUAD immunity.

Dataset Information

Integrated Analysis of Transcriptome and Prognosis Data Identifies FGF22 as a Prognostic Marker of Lung Adenocarcinoma.

Publications

Integrated Analysis of Transcriptome and Prognosis Data Identifies FGF22 as a Prognostic Marker of Lung Adenocarcinoma.

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