A Novel Risk Score Model of Lactate Metabolism for Predicting over Survival and Immune Signature in Lung Adenocarcinoma

Jiang, Zhou; Luo, Yong; Zhang, Lemeng; Li, Haitao; Pan, Changqie; Ye, Hua; Cheng, Tianli; Chen, Jianhua

doi:10.3390/cancers14153727

Cited by 15 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using 16 metabolic genes, a previous study constructed a prognostic model for LUAD and it reached an AUC value of 0.638 on 3-year prognosis prediction in the independent dataset (GSE37745) (He et al, 2020). Another study provided a risk score model for LUAD by six genes, and it reached an AUC value of 0.66 on 3-year prognosis prediction in GSE37745 (Jiang et al, 2022). In a LUAD prognosis model constructed by 27 hypoxia-related genes, it showed AUC values of 0.65 and 0.66 in the validation datasets (Ouyang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Zhuge

Wang

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis.Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066).Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively.Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Zhuge

Wang

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…The maximum number of clusters k = 9 and the optimal number of clusters were synthesised based on the cumulative distribution function (CDF) curve, consensus matrix, and consistent cluster score. PCA was then used in conjunction with t-distributed stochastic neighbour embedding (tSNE) to determine whether these genes could be used to distinguish samples ( 28 , 29 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis

Zhou

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

ObjectiveAn analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset.MethodsBased on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models.ResultsThe location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed.ConclusionThis study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Disease prediction models have been widely used to evaluate patient survival and other prognostic indicators in LUAD ( Jiang et al, 2020 ; Jiang et al, 2022 ). Recently, a novel model related to lactate metabolism for predicting overall survival and immune signature in LUAD was reported and different immune signatures were built ( Jiang et al, 2022 ). Moreover, an increasing number of LUAD prognostic biomarkers have been discovered by analyzing expression profiles from public databases and related clinical information ( Jiang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Characterization of lung adenocarcinoma based on immunophenotyping and constructing an immune scoring model to predict prognosis

Liu

Xiao

et al. 2022

Front. Pharmacol.

View full text Add to dashboard Cite

Background: Lung cancer poses great threat to human health, and lung adenocarcinoma (LUAD) is the main subtype. Immunotherapy has become first line therapy for LUAD. However, the pathogenic mechanism of LUAD is still unclear.Methods: We scored immune-related pathways in LUAD patients using single sample gene set enrichment analysis (ssGSEA) algorithm, and further identified distinct immune-related subtypes through consistent clustering analysis. Next, immune signatures, Kaplan-Meier survival analysis, copy number variation (CNV) analysis, gene methylation analysis, mutational analysis were used to reveal differences between subtypes. pRRophetic method was used to predict the response to chemotherapeutic drugs (half maximal inhibitory concentration). Then, weighted gene co-expression network analysis (WGCNA) was performed to screen hub genes. Significantly, we built an immune score (IMscore) model to predict prognosis of LUAD.Results: Consensus clustering analysis identified three LUAD subtypes, namely immune-Enrich subtype (Immune-E), stromal-Enrich subtype (Stromal-E) and immune-Deprived subtype (Immune-D). Stromal-E subtype had a better prognosis, as shown by Kaplan-Meier survival analysis. Higher tumor purity and lower immune cell scores were found in the Immune-D subtype. CNV analysis showed that homologous recombination deficiency was lower in Stromal-E and higher in Immune-D. Likewise, mutational analysis found that the Stromal-E subtype had a lower mutation frequency in TP53 mutations. Difference in gene methylation (ZEB2, TWIST1, CDH2, CDH1 and CLDN1) among three subtypes was also observed. Moreover, Immune-E was more sensitive to traditional chemotherapy drugs Cisplatin, Sunitinib, Crizotinib, Dasatinib, Bortezomib, and Midostaurin in both the TCGA and GSE cohorts. Furthermore, a 6-gene signature was constructed to predicting prognosis, which performed better than TIDE score. The performance of IMscore model was successfully validated in three independent datasets and pan-cancer.

show abstract

A Novel Risk Score Model of Lactate Metabolism for Predicting over Survival and Immune Signature in Lung Adenocarcinoma

Cited by 15 publications

References 42 publications

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis

Characterization of lung adenocarcinoma based on immunophenotyping and constructing an immune scoring model to predict prognosis

Contact Info

Product

Resources

About