Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Zhang, Lei; Zhang, Zhe; Yu, Zhenglun

doi:10.1186/s12967-019-02173-2

Cited by 168 publications

(157 citation statements)

References 37 publications

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“…These data were used to study the relationship between 15 genes and TME among patients with high versus low immune scores or stromal score groups using the ESTIMATE algorithm. Previous studies have reported that 9 out of the 15 immune genes associated with prognosis (S100A16, CRABP1, RBP2, FGF2, FPR2, BDNF, ANGPTL4, SEMA4B and VIPR1 [33][34][35][36][37][38][39][40][41]) are involved in the pathogenesis of lung cancer. The remaining six genes (IGKV4-1, IGKV6D-41, TNFRSF11A, INHA, IL11 and SCH3) have not been reported in the literature and may be used as potential biomarkers for lung cancer research.…”

Section: Discussionmentioning

confidence: 99%

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Qin

Yuan

et al. 2020

FEBS Open Bio

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The magnitude of the immune response is closely associated with clinical outcome in patients with cancer. However, finding potential therapeutic targets for lung cancer in the immune system remains challenging. Here, we constructed a vital immune‐prognosis genes (VIPGs) based cluster of lung adenocarcinoma (LUAD) from IMMPORT databases and The Cancer Genome Atlas. A transcription factor regulatory network for the VIPGs was also established. The tumor microenvironment of LUAD was analyzed using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and single‐sample Gene Set Enrichment Analysis. The immune checkpoints and genomic alterations were explored in the different immune clusters. We identified 15 VIPGs for patients with LUAD and clustered the patients into low‐immunity and high‐immunity subtypes. The immune score, stromal score and ESTIMATE score were significantly higher in the high‐immunity subtype, whereas tumor purity was higher in the low‐immunity subtype. In addition, the immune checkpoints cytotoxic T lymphocyte associate protein‐4(CTLA4), programmed cell death protein‐1 and programmed death‐ligand were elevated in the low‐immunity subtype. The genomic results also showed that the tumor mutation burden was higher in the high‐immunity subtype. Finally, Gene Set Enrichment Analysis showed that several immune‐related gene sets, including interleukin‐2/STAT5 signaling, inflammatory response, interleukin‐6/Janus kinase(JAK)/signal transducer and activator of transcription 3 (STAT3) signaling, interferon‐gamma response and allograft rejection, were elevated in the high‐immunity subtype. Finally, high‐immunity patients exhibited greater overall and disease‐specific survival outcome compared with low‐immunity patients (log rank P = 0.013 and P = 0.0097). Altogether, here we have identified 15 immune‐prognosis genes and a potential immune subtype for patients with LUAD, which may provide new insights into the prognosis and treatment of LUAD.

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Section: Discussionmentioning

confidence: 99%

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Qin

Yuan

et al. 2020

FEBS Open Bio

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“…Similarly, a study reported an immune-related signature [17], which AUC of 1-(0.78) and 3-(0.76) year was also lower than that of this study. In addition, Zhang et al constructed a glycolysis-related gene prognostic signature with the AUC = 0.72 [18]. Meanwhile, a research developed a autophagy-related gene prognostic signature with the AUC = 0.615 [19].…”

Section: Discussionmentioning

confidence: 99%

Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

2020

Preprint

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Background Lung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. The aim of this study was to establish an immune-related gene pairs (IRGPs) signature for predicting the prognosis of LUAD patients.Methods We downloaded the gene expression profile and immune-related gene set from TCGA and ImmPort database, respectively, to establish IRGPs. Then, IRGPs subjected to univariate Cox regression analysis, LASSO regression analysis and multivariable Cox regression analysis to screen and develop a IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from GEO was used to validate this signature.Results A IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in TCGA set was 0.867 and 0.870, respectively. Similar result was observed in the AUC of GEO set and Total set (GEO set [1-year: 0.819; 3-years: 0.803]; Total set [1-year: 0.845; 3-years: 0.801]). Survival analysis of three sets demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that risk score was independent prognostic factors.Conclusions We developed a novel IRGPs signature for predicting prognosis of LUAD.

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“…In the initiation and progression of hepatocellular carcinoma, genetic factors usually play an important role. Meanwhile, mRNA gene signature based on a certain characteristic like glycolysis [11] and immune [12] have been developed for prognosis of cancers. In this research, we explored specific function to identify genes by GSEA that could predict the survival of HCC patients.…”

Section: Discussionmentioning

confidence: 99%

A Novel Signature Based On Mtorc1 Pathway In Hepatocellular Carcinoma

Zhang

2020

Preprint

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BackgroundmTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature.MethodsHCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot.ResultsThe signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found significantly associated with the infiltration of B cells, CD4+T-cells, CD8+T-cells, dendritic cells, macrophages and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Finally, the nomogram involved age, gender, stage and signature have been established.ConclusionThe mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC. *These authors contributed equally.

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Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Cited by 168 publications

References 37 publications

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis

Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

A Novel Signature Based On Mtorc1 Pathway In Hepatocellular Carcinoma

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