Dataset Information

Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma.

ABSTRACT:

Background

Transforming growth factor (TGF)-β signaling functions importantly in regulating tumor microenvironment (TME). This study developed a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical outcome of patients with lung adenocarcinoma (LUAD).

Methods

TGF-β signaling-related genes came from The Molecular Signature Database (MSigDB). LUAD prognosis-related genes were screened from all the genes involved in TGF-β signaling using least absolute shrinkage and selection operator (LASSO) Cox regression analysis and then used to establish a risk score model for LUAD. ESTIMATE and CIBERSORT analyzed infiltration of immune cells in TME. Immunotherapy response was analyzed by the TIDE algorithm.

Results

A LUAD prognostic 5-gene signature was developed based on 54 TGF-β signaling-related genes. Prognosis of high-risk patients was significantly worse than low-risk patients. Both internal validation and external dataset validation confirmed a high precision of the risk model in predicting the clinical outcomes of LUAD patients. Multivariate Cox analysis demonstrated the model independence in OS prediction of LUAD. The risk model was significantly related to the infiltration of 9 kinds of immune cells, matrix, and immune components in TME. Low-risk patients tended to respond more actively to anti-PD-1 treatment, while high-risk patients were more sensitive to chemotherapy and targeted therapy.

Conclusions

The 5-gene signature based on TGF-β signaling-related genes showed potential for LUAD management.

SUBMITTER: Yu Q

PROVIDER: S-EPMC9172180 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma.

Yu Qian Q Zhao Liang L Yan Xue-Xin XX Li Ye Y Chen Xin-Yu XY Hu Xiao-Hua XH Bu Qing Q Lv Xiao-Ping XP

World journal of surgical oncology 20220606 1

<h4>Background</h4>Transforming growth factor (TGF)-β signaling functions importantly in regulating tumor microenvironment (TME). This study developed a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical outcome of patients with lung adenocarcinoma (LUAD).<h4>Methods</h4>TGF-β signaling-related genes came from The Molecular Signature Database (MSigDB). LUAD prognosis-related genes were screened from all the genes involved in TGF-β signaling using least absol ...[more]

PMID: 35668494

Similar Datasets

Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most frequent causes of tumor-related mortality worldwide. Recently, the role of N7-methylguanosine (m7G) in tumors has begun to receive attention, but no investigation on the impact of m7G on LUAD. This study aims to elucidate the significance of m7G on the prognosis and immunotherapy in LUAD.MethodsConsensus clustering was employed to determine the molecular subtype according to m7G-related regulators extracted from The Cancer Genome Atlas (TCGA) database. Survival, clinicopathological features and tumor mutational burden (TMB) analysis were applied to research molecular characteristics of each subtype. Subsequently, "limma" package was used to screen differentially expressed genes (DEGs) between subtypes. In the TCGA train cohort (n = 245), a prognostic signature was established by univariate Cox regression, lasso regression and multivariate Cox regression analysis according to DEGs and survival analysis was employed to assess the prognosis. Then the prognostic value of the signature was verified by TCGA test cohort (n = 245), TCGA entire cohort (n = 490) and GSE31210 cohort (n = 226). Moreover, the association among immune infiltration, clinical features and the signature was investigated. The immune checkpoints, TMB and tumor immune dysfunction and exclusion (TIDE) were applied to predict the immunotherapy response.ResultsTwo novel molecular subtypes (C1 and C2) of LUAD were identified. Compared to C2 subtype, C1 subtype had poorer prognosis and higher TMB. Subsequently, the signature (called the "m7G score") was constructed according to four key genes (E2F7, FAM83A, PITX3, and HOXA13). The distribution of m7G score were significantly different between two molecular subtypes. The patients with lower m7G score had better prognosis in TCGA train cohort and three verification cohort. The m7G score was intensively related to immune infiltration. Compared with the lower score, the higher m7G score was related to remarkable upregulation of the PD-1 and PD-L1, the higher TMB and the lower TIDE score.ConclusionThis study established a m7G-related signature for predicting prognosis and immunotherapy in LUAD, which may contribute to the development of new therapeutic strategies for LUAD.

Project description:AimsThe dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear.MethodsData on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA.ResultsIn glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway.ConclusionsA prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.

Project description:BackgroundLung cancer has the highest case fatality rate among cancers because of uncontrolled proliferation and early metastasis of cancer cells in the lung tissue. This study aimed to clarify the role of the non-SMC condensin I complex, subunit G (NCAPG) in lung adenocarcinoma (LUAD), explore the mechanisms of its progression, and lay the foundation for the search for new biological markers.MethodsWe analyzed overlapping differentially expressed genes (DEGs) from three datasets; a protein-protein interaction (PPI) network was subsequently constructed and analyzed using Cytoscape. We then selected NCAPG for validation because of its poor prognosis and because it has not been sufficiently studied in the context of LUAD. Immunohistochemical analysis was used to detect the expression of NCAPG in LUAD tissues, and the relationships between NCAPG and clinical parameters were analyzed. In vitro and in vivo experiments were conducted to verify the role of NCAPG in LUAD. Finally, we studied the specific mechanism of action of NCAPG in LUAD.ResultsThrough comprehensive analysis of the GSE43458, GSE75037, and The Cancer Genome Atlas databases, we identified 517 overlapping DEGs. Among them, NCAPG was identified as a hub gene. Immunohistochemical analysis revealed that NCAPG was strongly associated with the clinical stage, M-classification, and N-classification. Univariate and multivariate Cox regression analyses indicated that NCAPG was a prognostic risk factor for LUAD, while the in vitro experiments showed that NCAPG overexpression promoted proliferation, migration, invasion, and epithelial-mesenchymal transition. Furthermore, knockdown of NCAPG inhibited LUAD progression, both in vitro and in vivo. Mechanistically, NCAPG overexpression increased p-Smad2 and p-Smad3 expressions in the transforming growth factor β (TGF-β) signaling pathway. Additionally, rescue experiments indicated that TGF-β signaling pathway inhibitors could restore the effect of NCAPG overexpression in LUAD cells.ConclusionsNCAPG may promote proliferation and migration via the TGF-β signaling pathway in LUAD.

Project description:BackgroundLung adenocarcinoma (LUAD) stands as the foremost histological subtype of non-small-cell lung cancer, accounting for approximately 40% of all lung cancer diagnoses. However, there remains a critical unmet need to enhance the prediction of clinical outcomes and therapy responses in LUAD patients. Keratins (KRTs), serving as the structural components of the intermediate filament cytoskeleton in epithelial cells, play a crucial role in the advancement of tumor progression. This study investigated the prognostic significance of the KRT family gene and developed a KRT gene signature (KGS) for prognostic assessment and treatment guidance in LUAD.MethodsTranscriptome profiles and associated clinical details of LUAD patients were meticulously gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The KGS score was developed based on the expression of five prognostic KRT genes (KRT7, KRT8, KRT17, KRT18, and KRT80), and the upper quartile of the KGS score was chosen as the cutoff. The Kaplan-Meier method was evaluated to compare survival outcomes between KGS-high and KGS-low groups. The underlying mechanism was further investigated by GSEA, GSVA, and other bioinformatic algorithms.ResultsHigh expression of the KGS signature exhibited a robust association with poorer overall survival (OS) in the TCGA-LUAD dataset (HR: 1.81; 95% CI: 1.35-2.42, P = 0.00011). The association was further corroborated in three external GEO cohorts, including GSE31210 (HR: 3.31; 95% CI: 1.7-6.47, P = 0.00017), GSE72094 (HR: 1.95; 95% CI: 1.34-2.85, P = 0.00057) and GSE26939 (HR: 3.19; 95% CI: 1.74-5.84, P < 0.0001). Interestingly, KGS-high tumors revealed enrichments in TGF-β and WNT-β catenin signaling pathways, exhibited heightened activation of the epithelial-mesenchymal transition (EMT) pathway and proved intensified tumor stemness compared to their KGS-low counterparts. Additionally, KGS-high tumor cells exhibited increased sensitivity to several targeted agents, including gefitinib, erlotinib, lapatinib, and trametinib, in comparison to KGS-low cells.ConclusionThis study developed a KGS score that independently predicts the prognosis in LUAD. High expression of KGS score, accompanied by upregulation of TGF-β and WNT-β catenin signaling pathways, confers more aggressive EMT and tumor progression.

Dataset Information

Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets