Prognostic signature of lung adenocarcinoma based on stem cell-related genes

Huang, Zhanghao; Shi, Muqi; Zhou, Hao; Wang, Jinjie; Zhang, Haijian; Shi, Jia Hai

doi:10.1038/s41598-020-80453-4

Cited by 9 publications

(8 citation statements)

References 49 publications

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“…Hence, our EMT-associated gene signature was assessed by comparing with 2 other identified signatures: a 9-gene recurrence-associated signature 31 and a 10-gene prognostic signature. 32 The risk scores of each sample from the combined cohort of GSE31210 and GSE50081 were calculated using their formulas respectively ( Supplemental Table S7 ) and were subsequently used for the following analyses. The ROC curve of the 5-year recurrence prediction shows that our 5-gene signature demonstrated better performance than the 9-gene recurrence-associated signature ( Figure 7B , AUC = 0.781 vs AUC = 0.633, respectively).…”

Section: Resultsmentioning

confidence: 99%

An In Silico Analysis Reveals an EMT-Associated Gene Signature for Predicting Recurrence of Early-Stage Lung Adenocarcinoma

et al. 2022

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Background: The potential micrometastasis tends to cause recurrence of lung adenocarcinoma (LUAD) after surgical resection and consequently leads to an increase in the mortality risk. Compelling evidence has suggested the underlying mechanisms of tumor metastasis could involve the activation of an epithelial-mesenchymal transition (EMT) program. Hence, the objective of this study was to develop an EMT-associated gene signature for predicting the recurrence of early-stage LUAD. Methods: The mRNA expression data of patients with early-stage LUAD were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) available databases. Gene Set Variation Analysis (GSVA) was first performed to provide an assessment of EMT phenotype, whereas Weighted Gene Co-expression Network Analysis (WGCNA) was constructed to determine EMT-associated key modules and genes. Based on the genes, a novel EMT-associated signature for predicting the recurrence of early-stage LUAD was identified using a least absolute shrinkage and selection operator (LASSO) algorithm and a stepwise Cox proportional hazards regression model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves and Cox regression analyses were used to estimate the performance of the identified gene signature. Results: GSVA revealed diverse EMT states in the early-stage LUAD. Further correlation analyses showed that the EMT states presented high correlations with several hallmarks of cancers, tumor purity, tumor microenvironment cells, and immune checkpoint genes. More importantly, Kaplan-Meier survival analyses indicated that patients with high EMT scores had worse recurrence-free survival (RFS) and overall survival (OS) than those with low EMT scores. A novel 5-gene signature ( AGL, ECM1, ENPP1, SNX7, and TSPAN12) was established based on the EMT-associated genes from WGCNA and this signature successfully predicted that the high-risk patients had a higher recurrence rate compared with the low-risk patients. In further analyses, the signature represented robust prognostic values in 2 independent validation cohorts (GEO and TCGA datasets) and a combined GEO cohort as evaluated by Kaplan-Meier survival ( P-value < .0001) and ROC analysis (AUC = 0.781). Moreover, the signature was corroborated to be independent of clinical factors by univariate and multivariate Cox regression analyses. Interestingly, the combination of the signature-based recurrence risk and tumor-node-metastasis (TNM) stage showed a superior predictive ability on the recurrence of patients with early-stage LUAD. Conclusion: Our study suggests that patients with early-stage LUAD exhibit diverse EMT states that play a vital role in tumor recurrence. The novel and promising EMT-associated 5-gene signature identified and validated in this study may be applied to predict the recurrence of early-stage LUAD, facilitating risk stratification, recurrence monitoring, and individualized management for the patients after surgical resection.

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Section: Resultsmentioning

confidence: 99%

An In Silico Analysis Reveals an EMT-Associated Gene Signature for Predicting Recurrence of Early-Stage Lung Adenocarcinoma

et al. 2022

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“…Cancer stem cell, which is a small and rare subset of cells with stem cell-like characteristics, plays an important role in tumor proliferation, metastasis, and recurrence [ 22 ]. To observe the difference of stem cell infiltration between the two groups, we made a comparative analysis at the DNA and RNA levels.…”

Section: Methodsmentioning

confidence: 99%

Prognostic Risk Signature and Comprehensive Analyses of Endoplasmic Reticulum Stress-Related Genes in Lung Adenocarcinoma

Yang

Wei

et al. 2022

Journal of Immunology Research

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Lung adenocarcinoma (LUAD) is the main pathological subtype of non-small-cell lung cancer. Endoplasmic reticulum stress (ERS) has been found to be involved in multiple tumor-related biological processes. At present, a comprehensive analysis of ERS-related genes in LUAD is still lacking. A total of 1034 samples from TCGA and GEO were used to screen differentially expressed genes. Further, Random Forest algorithm was utilized to screen characteristic genes related to prognosis. Then, LASSO Cox regression was used to construct a prognostic signature. Taking the median of signature score as the threshold, patients were separated into high-risk (HR) group and low-risk (LR) group. Tumor mutation burden (TMB), immune cell infiltration, cancer stem cell infiltration, expression of HLA, and immune checkpoints of the two risk groups were analyzed. TIDE score was used to evaluate the response of the two risk groups to immunotherapy. Finally, the gene expression was verified in clinical tissues with RT-qPCR. An eight-gene signature (ADRB2, AGER, CDKN3, GJB2, SFTPC, SLC2A1, SLC6A4, and SSR4) was constructed. TMB and cancer stem cell infiltration were higher in the HR group than the LR group. TIDE score and expression level of HLA were higher in the LR group than the HR group. Expression level of immune checkpoints, including CD28, CD27, IDO2, and others, were higher in the LR group. Multiple drugs approved by FAD, targeting ERS-related genes, were available for the treatment of LUAD. In summary, we established a stable prognostic model based on ERS-related genes to help the classification of LUAD patients and looked for new treatment strategies from aspects of immunity, tumor mutation, and tumor stem cell infiltration.

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“…More importantly, these genes were not closely related to tumor metastasis or recurrence, which suggests that these gene signatures may fail in predicting the OS rate and recurrence-free survival of LUAD patients. So we compare our signature with two other signatures, which are constructed by glycolysis-related genes [33] and stem cell-related genes [34]. As expected, the signature constructed by EMT-related genes has a better stability and accuracy in predicting the OS of LUAD patients.…”

Section: Discussionmentioning

confidence: 80%

“…Comparison with other signatures of LUAD: There are some already published prognostic signatures for lung adenocarcinoma, but the genes they used to build the model were not so strongly associated with cancer development, and they didn't compare it with other signature. Therefore, We calculate the risk scores of patients in the two additional databases, GSE30219 and GSE31210 [32], using the method applied in this paper and the method mentioned in Liu et al [33] and Huang et al [34], respectively. It can be clearly seen from the results that the signature constructed in this paper has a better prediction effect, while the signature in Liu et al only has a good predict performance in GSE31210 data set, and the signature in Huang et al has a poor predict performance in both data sets (Supplementary Figure 2).…”

Section: Clinical Characteristics Of Patientsmentioning

confidence: 99%

A novel epithelial-mesenchymal transition-related gene signature for prognosis prediction in patients with lung adenocarcinoma

Feng

Huang

Guo

et al. 2022

Heliyon

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Traditional pathological diagnoses and clinical methods are insufficient to accurately predict the prognosis of lung adenocarcinoma (LUAD). Epithelial-mesenchymal transition (EMT) process is closely related to tumor cell migration. However, the prognostic value of EMT-related genes in LUAD is still unclear. In this study, we collected bulk RNA-sequencing (RNA-seq) and microarray data of LUAD patients from public databases and identified different expressed EMT-related genes in tumor and normal tissues. Then, we used the least absolute shrinkage and selection operator Cox regression model to develop a multigene signature in the cancer genome atlas (TCGA) cohort and validated the model in the OncoSG (Singapore Oncology Data Portal) cohort as well as other datasets. Finally, we constructed a 12-gene signature to divide LUAD patients into high-risk and low-risk groups of overall survival (OS), which has a better stability and accuracy in predicating the OS of patients compared with some other published signatures of LUAD. In addition, evaluation of the risk model using the time-related receiver operating characteristic (ROC) curve confirmed the predictive ability of the model. Functional analysis showed that these genes are related to immunity. CD8 T cell and CD4 T cell types were significantly negatively correlated with the risk score in the analysis of immune infiltration. In general, our model provides useful information that may help clinicians better predict the prognosis of LUAD patients and provides potential targets for immunotherapy of LUAD.

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Prognostic signature of lung adenocarcinoma based on stem cell-related genes

Cited by 9 publications

References 49 publications

An In Silico Analysis Reveals an EMT-Associated Gene Signature for Predicting Recurrence of Early-Stage Lung Adenocarcinoma

An In Silico Analysis Reveals an EMT-Associated Gene Signature for Predicting Recurrence of Early-Stage Lung Adenocarcinoma

Prognostic Risk Signature and Comprehensive Analyses of Endoplasmic Reticulum Stress-Related Genes in Lung Adenocarcinoma

A novel epithelial-mesenchymal transition-related gene signature for prognosis prediction in patients with lung adenocarcinoma

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