Watch quickly! Glutathione metabolism and bioinformatics, minimalist approach+easy analysis, coupled with amino acid metabolism hotspots to achieve high scores
research summary
The aim of this study is to develop a prognostic model using long non coding RNAs (lncRNAs) related to glutathione (GSH) metabolism to predict the prognosis of lung adenocarcinoma (LUAD) patients and evaluate tumor immunity.
Researchers analyzed survival data from the Cancer Genome Atlas (TCGA) and identified lncRNAs related to glutathione metabolism through Pearson correlation analysis.
They constructed a prognostic model using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) methods and validated it through multiple analyses.
Functional analysis revealed differences in immune infiltration and drug sensitivity, and in vitro experiments confirmed the role of lnc-AL162632.3 in LUAD.
The model consisting of 9 lncRNAs was ultimately determined to be effective in predicting patient survival.
High risk patients have a higher burden of tumor mutations and stemness, providing potential evidence for personalized immunotherapy.
Research result
Research process
The author showed the flow chart of this study (Figure 1), including the identification of 16,876 lncRNAs from 541 cancer patient samples, identification of 87 GSH-related genes, co-expression analysis, univariate COX analysis, LASSO-COX analysis to construct the risk prognosis model.
As well as a variety of subsequent functional experiments, immune infiltration analysis, tumor mutation burden analysis, drug sensitivity analysis and so on.
The flow chart presents the overall context and key steps of the study.
Through analysis, the authors identified 1748 lncRNAs associated with GSH metabolism (Figure 2A).
Ten fold cross-validation and LASSO-COX analysis identified nine lncRNAs that were significantly associated with prognosis (Figure 2B, C).
At the same time, the authors drew a heat map of the correlation between lncRNAs and gene sets (Figure 2D) to show the relationship between them.
These results laid a foundation for the subsequent construction of prognostic models.
Survival analysis and verification
The results of K-M analysis by the authors showed that the overall survival (OS) of high-risk patients in the training set, validation set and overall cohort was significantly lower than that of low-risk patients (Figure 3A-C).
Progression-free survival (PFS) analysis results were consistent with OS results (Figure 3D).
The risk curve showed that risk score was positively correlated with patient mortality, and with the increase of risk score, the expression of some lncRNAs such as AL162632.3 increased, while the expression of some lncRNAs such as AL078590.2 decreased (FIG. 3E-G).
These results indicate that the constructed risk model can effectively distinguish the survival of patients with different risks.
Independent prognostic analysis and PCA analysis
Univariate and multivariate Cox regression analysis showed that the model had a hazard ratio (HRs) of more than 1, with a highly significant P-value (Figures 4A, B).
ROC curve analysis showed that the AUC and C-index values of this prognostic model were higher in predicting patient outcomes than clinical factors (Figure 4C-E).
PCA analysis showed that the developed lncRNA model was able to distinguish patients well (Figure 4F-I).
These results verify the independence and predictive ability of the model.
Nomogram development and validation
The authors constructed a clinical nomogram (Figure 5A) with a corresponding score for each clinical factor, and the total score could be used to estimate a patient’s 1 -, 3 -, and 5-year survival probability.
The authors drew calibration curves (FIG. 5B) to verify the accuracy of their predictions, and performed OS analysis for patients with different cancer stages, showing that high-low risk scores significantly differentiated survival duration in both early and late stages (FIG. 5C, D).
The nomogram helps to personalize prognostic stratification of LUAD patients.
Gene function enrichment analysis
The authors analyzed differences between the high – and low-risk groups and identified 447 significant genes.
GO functional analysis showed that differentially expressed genes were mainly concentrated in immune cell infiltration, immune activation, extracellular matrix remodeling, and cytokine-cytokine receptor interactions (Figure 6A, B).
KEGG pathway analysis showed that these genes were enriched in pathways regulating immune cells, such as ras signaling pathway and pi3k-akt signaling pathway (Figure 6C).
GSEA analysis showed that most lncRNAs in the model were associated with cancer, immune, and metabolism-related pathways (Figure 6D).
These results reveal a potential mechanism by which key lncRNAs influence survival and prognosis in LUAD.
Immunocorrelation analysis
The authors showed the proportion of typical immune cells in samples from LUAD patients (Figure 7A), and found that the immune cell score and overall estimated score in TME of high-risk patients were lower than those of low-risk patients through multiple algorithm analysis.
Moreover, there were significant differences in classical immune functions such as type II IFN response, type I IFN response, and HLA (Figure 7B-E).
The authors’ analysis showed a positive correlation between risk score and tumor stemness (Figure 7F).
These results suggest that risk scores are closely related to immune function and tumor stemness.
Tumor mutation burden (TMB) detection
The authors’ analysis found that the mutation rate of key genes such as TP53, KRAS and COL11A1 was higher in the high-risk group (Figure 8A, B), and the tumor mutation burden in the high-risk group was significantly higher than that in the low-risk group (Figure 8C).
Kaplan-Meier analysis showed that patients with high TMB had significantly longer survival than those with low TMB (Figure 8D).
Patients were grouped by risk score and TMB level to analyze survival differences, and it was found that high TMB and low risk groups survived the longest, and low TMB and high risk groups survived the shortest (Figure 8E).
TIDE scores were significantly lower in the high-risk cohort (Figure 8F).
These results suggest that TMB is strongly associated with risk score and patient survival, and that high-risk groups may respond differently to immunotherapy.
Drug sensitivity analysis
The authors used the oncoPredict software package to screen potentially sensitive drugs, and the results showed significant differences in the IC50 values of cisplatin, docetaxel, gemcitabine, vinorelbine, and paclitaxel (Figure 9).
This provides a reference for the selection of personalized chemotherapy drugs for lung adenocarcinoma patients.
Validation of lncRNAs expression in vitro cell experiments
The expression levels of lncRNAs with independent prognostic significance were verified by qPCR.
Compared with normal cell lines HBE, the expression of AL162632.3 was significantly increased in PC9, H1299, A549 and H1975 cancer cell lines, and the most obvious in H1299 cells.
The expression of LINC01711 increased or decreased in some lung cancer cell lines.
There was no significant change in the expression of GSEC in these cancer cell lines.
AC026355.2 was significantly down-regulated in all four cancer cell lines;
The expression of AL096701.4 decreased in some cell lines and increased in H1975 (Figures 10A, B).
These results confirmed the differences in the expression of lncRNAs in different cell lines in the model.
Down-regulating NRC-AL162632.3 inhibited the growth of LUAD
The authors used RNAi technology to effectively knock down the highly expressed LCC-AL162632.3 in H1299 and A549 lung cancer cell lines, and CCK8 proliferation and colony formation experiments showed that knocking down LCC-AL162632.3 significantly reduced cell growth (FIG. 11C, D, E, F).
These results indicate that lnc – AL162632.3 can promote the growth of lung cancer cells.
Down-regulating NRC-AL162632.3 inhibited LUAD migration and invasion
The authors further demonstrated, through wound healing and invasion experiments, that inhibition of NRC-AL162632.3 significantly impaired the motor capacity of cancer cells (Figures 12A, B, C).
In vivo experiments showed that the volume and weight of subcutaneous tumors in nude mice knocked down LCC-AL162632.3 were significantly smaller than those in the control group (Figure 12D-F).
These results suggest that NRC-AL162632.3 plays an important role in the migration and invasion of lung cancer cells.
Research summary
This study focused on lung adenocarcinoma to investigate the role of glutathione (GSH) metabolically associated long non-coding Rnas (lncRNAs).
The researchers obtained data from the TCGA database, identified 9 lncRNAs associated with GSH metabolism through a series of analyses, and constructed a prognostic model.
This model can effectively predict patient survival, and high-risk patients have higher tumor mutation burden and dryness.
Functional analysis revealed that key lncRNAs are related to immune regulation, and the composition and function of immune cells in high and low risk groups are different, and the sensitivity to chemotherapy drugs is different.
In vitro experiments confirmed that lnc – AL162632.3 promoted the proliferation, migration and invasion of lung cancer cells.
Although the study has limitations, this model and related findings provide a potential basis for personalized immunotherapy for lung adenocarcinoma to help improve patient outcomes.
