Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma

In recent years, personalized cancer immunotherapy, especially stratification-driven precision treatments have gained significant traction. However, due to the heterogeneity in clinical cohorts, the uncombined analysis of stratification/therapeutics may lead to confusion in determining ideal therapeutic options. We report that the coupled immune stratification and drug repurposing could facilitate identification of therapeutic candidates in patients with lung adenocarcinoma (LUAD). First, we categorized the patients into four groups based on immune gene profiling, associated with distinct molecular characteristics and clinical outcomes. Then, the weighted gene co-expression network analysis (WGCNA) algorithm was used to identify co-expression modules of each groups. We focused on C3 group which is characterized by low immune infiltration (cold tumor) and wild-type EGFR, posing a significant challenge for treatment of LUAD. Five drug candidates against the C3 status were identified which have potential dual functions to correct aberrant immune microenvironment and also halt tumorigenesis. Furthermore, their steady binding affinity against the targets was verified through molecular docking analysis. In sum, our findings suggest that such coupled analysis could be a promising methodology for identification and exploration of therapeutic candidates in the practice of personalized immunotherapy.

Development and validation of an immune-related prognostic signature in lung adenocarcinoma

BACKGROUND

Lung adenocarcinomas (LUAD) is the most common histological subtype of lung cancers. Tumor immune microenvironment (TIME) is involved in tumorigeneses, progressions, and metastases. This study is aimed to develop a robust immune-related signature of LUAD.

METHODS

A total of 1774 LUAD cases sourced from public databases were included in this study. Immune scores were calculated through ESTIMATE algorithm and weighted gene co-expression network analysis (WGCNA) was applied to identify immune-related genes. Stability selections and Lasso COX regressions were implemented to construct prognostic signatures. Validations and comparisons with other immune-related signatures were conducted in independent Gene Expression Omnibus (GEO) cohorts. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ImmuCellAI and gene set enrichment analysis (GSEA).

RESULTS

In Cancer Genome Atlas (TCGA) LUAD cohorts, immune scores of higher levels were significantly associated with better prognoses (P < .05). Yellow (n = 270) and Blue (n = 764) colored genes were selected as immune-related genes, and after univariate Cox regression analysis (P < .005), a total of 133 genes were screened out for subsequent model constructions. A four-gene signature (ARNTL2, ECT2, PPIA, and TUBA4A) named IPSLUAD was developed through stability selection and Lasso COX regression. It was suggested by multivariate and subgroup analyses that IPSLUAD was an independent prognostic factor. It was suggested by Kaplan-Meier survival analysis that eight out of nine patients in high-risk groups had significantly worse prognoses in validation data sets (P < .05). IPSLUAD outperformed other signatures in two independent cohorts.

CONCLUSIONS

A robust immune-related prognostic signature with great performances in multiple LUAD cohorts was developed in this study.

Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD

Lung cancer has been the focus of attention for many researchers in recent years for the leading contribution to cancer-related death worldwide, in which lung adenocarcinoma (LUAD) is the most common histological type. However, the potential mechanism behind LUAD initiation and progression remains unclear. Aiming to dissect the tumor microenvironment of LUAD and to discover more informative prognosis signatures, we investigated the immune-related differences in three types of genetic or epigenetic characteristics (expression status, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and both the immune-related metabolic and neural systems by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct the prognostic prediction model. For the prognostic predictions on the independent test set, the performance of the trained models (average concordance index [C-index] = 0.839) is satisfied, with average 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.796, 0.786, and 0.777. Finally, the overall model was constructed based on all samples, which comprised 27 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.861), 3-year (AUC = 0.850), and 5-year (AUC = 0.916) survival predictions.

High expression levels of pyrimidine metabolic rate-limiting enzymes are adverse prognostic factors in lung adenocarcinoma: a study based on The Cancer Genome Atlas and Gene Expression Omnibus datasets

Reprogramming of metabolism is described in many types of cancer and is associated with the clinical outcomes. However, the prognostic significance of pyrimidine metabolism signaling pathway in lung adenocarcinoma (LUAD) is unclear. Using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, we found that the pyrimidine metabolism signaling pathway was significantly enriched in LUAD. Compared with normal lung tissues, the pyrimidine metabolic rate–limiting enzymes were highly expressed in lung tumor tissues. The high expression levels of pyrimidine metabolic–rate limiting enzymes were associated with unfavorable prognosis. However, purinergic receptors P2RX1, P2RX7, P2RY12, P2RY13, and P2RY14 were relatively downregulated in lung cancer tissues and were associated with favorable prognosis. Moreover, we found that hypo-DNA methylation, DNA amplification, and TP53 mutation were contributing to the high expression levels of pyrimidine metabolic rate–limiting enzymes in lung cancer cells. Furthermore, combined pyrimidine metabolic rate–limiting enzymes had significant prognostic effects in LUAD. Comprehensively, the pyrimidine metabolic rate–limiting enzymes were highly expressed in bladder cancer, breast cancer, colon cancer, liver cancer, and stomach cancer. And the high expression levels of pyrimidine metabolic rate–limiting enzymes were associated with unfavorable prognosis in liver cancer. Overall, our results suggested the mRNA levels of pyrimidine metabolic rate–limiting enzymes CAD, DTYMK, RRM1, RRM2, TK1, TYMS, UCK2, NR5C2, and TK2 were predictive of lung cancer as well as other cancers.

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma

Background

A hypoxia microenvironment plays a role in the initiation and progression of many cancer types, but its involvement in lung adenocarcinoma is still unclear. This study aimed to explore the potential correlation between hypoxia and lung adenocarcinoma and establish the hypoxia-associated gene signature in lung adenocarcinoma.

Methods

Lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The genes to be included in the hypoxia-associated signature were selected by performing univariate Cox regression analysis and lasso regression analysis. Then, the gene signature was verified by performing a survival analysis and constructing the multiple receiver operating characteristic (ROC) curve. The CIBERSORT tool was then used to investigate the potential correlation between the gene signature and immune cells. Moreover, a nomogram was constructed and evaluated by calculating the C-index.

Results

Four genes (XPNPEP1, ANGPTL4, SLC2A1, and PFKP) were included in the final signature. The results showed that patients in the high-risk group showed worse survival than those in the low-risk group. Moreover, we found two types of immune cells (memory activated CD4⁺ T cell and M0 macrophages) which showed a significant infiltration in the tissues of the high-risk group patients.

Conclusion

The hypoxia-associated gene signature established and validated in this study could be used as a potential prognostic factor in lung adenocarcinoma and may guide the immunotherapy choice.

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.

BTK Has Potential to Be a Prognostic Factor for Lung Adenocarcinoma and an Indicator for Tumor Microenvironment Remodeling: A Study Based on TCGA Data Mining

Tumor microenvironment (TME) plays a crucial role in the initiation and progression of lung adenocarcinoma (LUAD); however, there is still a challenge in understanding the dynamic modulation of the immune and stromal components in TME. In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cell (TIC) and the amount of immune and stromal components in 551 LUAD cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, Bruton tyrosine kinase (BTK) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that BTK expression was negatively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and positively correlated with the survival of LUAD patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression BTK group were mainly enriched in immune-related activities. In the low-expression BTK group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that B-cell memory and CD8+ T cells were positively correlated with BTK expression, suggesting that BTK might be responsible for the preservation of immune-dominant status for TME. Thus, the levels of BTK might be useful for outlining the prognosis of LUAD patients and especially be a clue that the status of TME transition from immune-dominant to metabolic activity, which offered an extra insight for therapeutics of LUAD.

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

BACKGROUND

There has been no report of prognostic signature based on immune-related genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA).

METHODS

RNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified via Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n = 284) and test datasets (n = 119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset, and external dataset GSE13507 (n = 165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BLCA.

RESULTS

A set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes [hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6-19, P < 0.001]. The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754, and 0.772 at 1-, 3-, and 5-year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4+ T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit.

CONCLUSION

The present immune-related signature can effectively classify BLCA patients into high-risk and low-risk groups in terms of survival rate, which may help select high-risk BLCA patients for more intensive treatment.