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A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.

ABSTRACT: Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

SUBMITTER: Liljedahl H

PROVIDER: S-EPMC7689824 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.

Liljedahl Helena H Karlsson Anna A Oskarsdottir Gudrun N GN Salomonsson Annette A Brunnström Hans H Erlingsdottir Gigja G Jönsson Mats M Isaksson Sofi S Arbajian Elsa E Ortiz-Villalón Cristian C Hussein Aziz A Bergman Bengt B Vikström Anders A Monsef Nastaran N Branden Eva E Koyi Hirsh H de Petris Luigi L Patthey Annika A Behndig Annelie F AF Johansson Mikael M Planck Maria M Staaf Johan J

International journal of cancer 20200812 1

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP ...[more]

PMID: 32745259

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Project description:Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD. Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model. Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p < 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4+ T cells and dendritic cell infiltration levels. Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.

Project description:Rationale: As the transcriptional products of active enhancers, enhancer RNAs (eRNAs) are essential for the initiation of tumorigenesis. However, the landscape and functional characteristics of eRNAs in Chinese lung adenocarcinoma, and the clinical utility of eRNA-based molecular subtypes remain largely unknown. Methods: A genome-wide profiling of eRNAs was performed in 80 Chinese lung adenocarcinoma patients with RNA-seq data. Functional eRNAs and associated genes were identified between paired adenocarcinoma and adjacent samples. Unsupervised clustering of functional eRNAs was conducted and the associations with molecular characteristics and clinical outcomes were accessed by integrating whole-genome sequencing data and clinical data. Additionally, 481 lung adenocarcinoma patients were used for the validation based on The Cancer Genome Atlas (TCGA) dataset. Results: A total of 3297 eRNAs with sufficient expression were identified, which were globally upregulated in adenocarcinoma samples compared to matched-adjacent pairs (P = 7.61×10-3). Further analyses indicated that these upregulated eRNAs were correlated with copy number amplification (CNA) status (Cor = 0.22, P = 0.045), and eRNA-correlated genes were primarily involved in cell cycle and immune system-related pathways. Based on the co-expression analysis of eRNAs with protein-coding genes, we defined 188 functional eRNAs and their correlated genes were overrepresented in cancer driver genes (ER = 1.98, P = 5.95×10-12) and clinically-actionable genes (ER = 2.19, P = 3.44×10-4). The eRNA-based consensus clustering further identified a novel molecular subtype with immune deficiency and a high-level of genomic alterations, which was associated with poor clinical outcomes of lung adenocarcinoma patients (OS: HR = 1.91, P = 0.015; PFI: HR = 1.64, P = 0.034). Conclusions: The genome-wide identification and characterization of eRNAs reveal novel regulators for the development of lung cancer, which provides a new biological dimension for the understanding of eRNAs during lung carcinogenesis and emphasize the clinical utility of eRNA-based molecular subtypes in the treatment of lung adenocarcinoma.

Project description:BackgroundMetabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.MethodGenes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.ResultTwo metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.ConclusionThis work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

Dataset Information

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.

Publications

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.

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