Gene expression-based clinical predictions in lung adenocarcinoma

Identification of prognostic values of the transcription factor-CpG-gene triplets in lung adenocarcinoma: A narrative review

OBJECTIVE

Abnormal DNA methylation can regulate carcinogenesis in lung adenocarcinoma (LUAD), while transcription factors (TFs) mediate methylation in a site-specific manner to affect downstream transcriptional regulation and tumor progression. Therefore, this study aimed to explore the TF-methylation-gene regulatory relationships that influence LUAD prognosis.

METHODS

Differential analyses of methylation sites and genes were generated by integrating transcriptome and methylome profiles from public databases. Through target gene identification, motif enrichment in the promoter region, and TF prediction, TF-methylation and methylation-gene relation pairs were obtained. Then, the prognostic TF-methylation-gene network was constructed using univariate Cox regression analysis. Prognostic models were constructed based on the key regulatory axes. Finally, Kaplan-Meier curves were created to evaluate the model efficacy and the relationship between candidate genes and prognosis.

RESULTS

A total of 1878 differential expressed genes and 1233 differential methylation sites were screened between LUAD and normal samples. Then 10 TFs were predicted to bind 144 enriched motifs. After integrating TF-methylation and methylation-gene relations, a prognostic TF-methylation-gene network containing 4 TFs, 111 methylation sites, and 177 genes was constructed. In this network, ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were selected to construct the prognostic models, which showed robust abilities in predicting 1-, 3-, and 5-year survival probabilities. Finally, ERG and MTURN were downregulated in LUAD samples, whereas FOXM1 and PTPR were upregulated. Their expression levels were related to LUAD prognosis.

CONCLUSION

ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were proposed as potential biomarkers for predicting the prognosis of LUAD.

A Distinct Glucose Metabolism Signature of Lung Adenocarcinoma With Prognostic Value

Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is the main cause of cancer-related death worldwide. Reprogramming of glucose metabolism plays a crucial role in tumorigenesis and progression. However, the regulation of glucose metabolism is still being explored in LUAD. Determining the underlying clinical value of glucose metabolism will contribute in increasing clinical interventions. Our study aimed to conduct a comprehensive analysis of the landscape of glucose metabolism-related genes in LUAD and develop a prognostic risk signature.

Methods: We extracted the RNA-seq data and relevant clinical variants from The Cancer Genome Atlas (TCGA) database and identified glucose metabolism-related genes associated with the outcome by correlation analysis. To generate a prognostic signature, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed.

Results: Finally, ten genes with expression status were identified to generate the risk signature, including FBP2, ADH6, DHDH, PRKCB, INPP5J, ABAT, HK2, GNPNAT1, PLCB3, and ACAT2. Survival analysis indicated that the patients in the high-risk group had a worse survival than those in the low-risk group, which is consistent with the results in validated cohorts. And receiver operating characteristic (ROC) curve analysis further validated the prognostic value and predictive performance of the signature. In addition, the two risk groups had significantly different clinicopathological characteristics and immune cell infiltration status. Notably, the low-risk group is more likely to respond to immunotherapy.

Conclusion: Overall, this study systematically explored the prognostic value of glucose metabolism and generated a prognostic risk signature with favorable efficacy and accuracy, which help select candidate patients and explore potential therapeutic approaches targeting the reprogrammed glucose metabolism in LUAD.

A gene-based survival score for lung adenocarcinoma by multiple transcriptional datasets analysis

Background

Lung adenocarcinoma (LUAD) remains a crucial factor endangering human health. Gene-based clinical predictions could be of great help for cancer intervention strategies. Here, we tried to build a gene-based survival score (SS) for LUAD via analyzing multiple transcriptional datasets.

Methods

We first acquired differentially expressed genes between tumors and normal tissues from intersections of four LUAD datasets. Next, survival-related genes were preliminarily unscrambled by univariate Cox regression and further filtrated by LASSO regression. Then, we applied PCA to establish a comprehensive SS based on survival-related genes. Subsequently, we applied four independent LUAD datasets to evaluate prognostic prediction of SS. Moreover, we explored associations between SS and clinicopathological features. Furthermore, we assessed independent predictive value of SS by multivariate Cox analysis and then built prognostic models based on clinical stage and SS. Finally, we performed pathway enrichments analysis and investigated immune checkpoints expression underlying SS in four datasets.

Results

We established a 13 gene-based SS, which could precisely predict OS and PFS of LUAD. Close relations were elicited between SS and canonical malignant indictors. Furthermore, SS could serve as an independent risk factor for OS and PFS. Besides, the predictive efficacies of prognostic models were also reasonable (C-indexes: OS, 0.7; PFS, 0.7). Finally, we demonstrated enhanced cell proliferation and immune escape might account for high clinical risk of SS.

Conclusions

We built a 13 gene-based SS for prognostic prediction of LUAD, which exhibited wide applicability and could contribute to LUAD management.