Development of an Oncogenic Driver Alteration Associated Immune-Related Prognostic Model for Stage I-II Lung Adenocarcinoma

The role of oncogenes and tumor suppressor genes in determining survival rates of lung cancer patients in the population of North Sumatra, Indonesia

Background: Gaining a better understanding of molecular alterations in the pathogenesis of lung cancer reveals a significant change in approach to the management and prognosis of lung cancer. Several oncogenes and tumor suppressor genes have been identified and have different roles related to survival rates in lung cancer patients. This study aims to determine the role of KRAS, EGFR, and TP53 mutations in the survival rate of lung cancer patients in the population of North Sumatra. Methods: This is a retrospective cohort study involving 108 subjects diagnosed with lung cancer from histopathology specimens. DNA extractions were performed using FFPE followed by PCR examinations for assessing the expressions of EGFR, RAS, and TP53 protein. Sequencing analysis was carried out to determine the mutations of EGFR exon 19 and 21, RAS protein exon 2, and TP53 exon 5-6 and 8-9. Data input and analysis were conducted using statistical analysis software for Windows. The survival rate analysis was presented with Kaplan Meier. Results: 52 subjects completed all procedures in this study. Most of the subjects are male (75%), above 60 years old (53.8%), heavy smokers (75%), and suffer from adenocarcinoma type of lung cancer (69.2%). No subjects showed KRAS exon 2 mutations. Overall survival rates increased in patients with EGFR mutations (15 months compared to 8 months; p=0.001) and decreased in patients with TP53 mutations (7 months compared to 9 months; p=0.148). Also, there was increasing Progression-Free Survival in patients with EGFR mutations (6 months compared to 3 months) ( p=0.19) and decreasing PFS in patients with TP53 mutations (3 months compared to 6 months) ( p=0.07). Conclusions: There were no KRAS mutations in this study. EGFR mutations showed a higher survival rate, while TP53 mutations showed a lower survival rate in overall survival and progression-free survival.

Identification of a m6A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma

BACKGROUND

Both N6-methyladenosine (m⁶A) and ferroptosis-related genes are associated with the prognosis of lung adenocarcinoma. However, the predictive value of m⁶A-related ferroptosis genes remains unclear. Here, we aimed to identify the prognostic value of m⁶A-related ferroptosis genes in lung adenocarcinoma.

METHODS

Lung adenocarcinoma sample data were downloaded from the University of California Santa Cruz Xena and Gene Expression Omnibus databases. Spearman's correlation analysis was used to screen for m⁶A-related ferroptosis genes. Univariate Cox regression, Kaplan-Meier, and Lasso analyses were conducted to identify prognostic m⁶A-related ferroptosis genes, and stepwise regression was used to construct a prognostic gene signature. The predictive value of the gene signature was assessed using a multivariate Cox analysis. In the validation cohort, survival analysis was performed to verify gene signature stability. The training cohort was divided into high- and low-risk groups according to the median risk score to assess differences between the two groups in terms of gene set variation analysis, somatic mutations, and tumor immune infiltration cells.

RESULTS

Six m⁶A-related ferroptosis genes were used to construct a gene signature in the training cohort and a multivariate Cox analysis was conducted to determine the independent prognostic value of these genes in lung adenocarcinoma. In the validation cohort, Kaplan-Meier and receiver operating characteristic analyses confirmed the strong predictive power of this signature for the prognosis of lung adenocarcinoma. Gene set variation analysis showed that the low-risk group was mainly related to immunity, and the high-risk group was mainly related to DNA replication. Somatic mutation analysis revealed that the TP53 gene had the highest mutation rate in the high-risk group. Tumor immune infiltration cell analysis showed that the low-risk group had higher levels of resting CD4 memory T cells and lower levels of M0 macrophages.

CONCLUSION

Our study identified a novel m⁶A-related ferroptosis-associated six-gene signature (comprising SLC2A1, HERPUD1, EIF2S1, ACSL3, NCOA4, and CISD1) for predicting lung adenocarcinoma prognosis, yielding a useful prognostic biomarker and potential therapeutic target.

A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644-0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586-0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571-0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.

Relationships of N6-Methyladenosine-Related Long Non-Coding RNAs With Tumor Immune Microenvironment and Clinical Prognosis in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and is associated with very high mortality. Emerging studies have shown that N6-methyladenosine (m6A)-related long non-coding (lnc) RNAs play crucial roles in tumor prognosis and the tumor immune microenvironment (TME). We aimed to explore the expression patterns of different m6A-related lncRNAs concerning patient prognosis and construct an m6A-related lncRNA prognostic model for LUAD. Methods: The prognostic value of m6A-related lncRNAs was investigated in LUAD samples from The Cancer Genome Atlas (TCGA). Potential prognostic m6A-related lncRNAs were selected by Pearson's correlation and univariate Cox regression analysis. Patients were divided into clusters using principal component analysis and the m6A-related lncRNA prognostic signature was calculated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results: Based on 91 prognostic m6A-related lncRNAs, we identified two m6A-related-lncRNA pattern clusters with different overall survival (OS) and different TMEs. We subsequently verified our findings multidimensionally by constructing a 13 m6A-related lncRNA prognostic signature (m6A-LPS) to calculate the risk score, which was robust in different subgroups. The receiver operating characteristic (ROC) curves and concordance index demonstrated that m6A-LPS harbored a promising ability to predict OS in TCGA data set and independent GSE11969 cohort. The risk score was also related to OS, TME, and clinical stage, and the risk score calculated by our model was also identified as independent prognostic predictive factors for LUAD patients after adjustment for age, smoking, gender, and stage. Enrichment analysis indicated that malignancy and drug resistance-associated pathways were more common in cluster2 (LUAD-unfavorable m6A-LPS). Furthermore, the results indicated that the signaling pathway enriched by the target gene of 13 m6A-related lncRNAs may be associated with metastasis and progression of cancer according to current studies. Conclusion: The current results indicated that different m6A-related-lncRNA patterns could affect OS and TME in patients with LUAD, and the prognostic signature based on 13 m6A-related lncRNAs may help to predict the prognosis in LUAD patients.

Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas

AIM

This study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms.

METHODS

The data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358).

RESULTS

A total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells.

CONCLUSION

The proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.

DNA Methylation Regulator-Meditated Modification Patterns Define the Distinct Tumor Microenvironment in Lung Adenocarcinoma

Background

Epigenetic changes of lung adenocarcinoma (LUAD) have been reported to be a relevant factor in tumorigenesis and cancer progression. However, the molecular mechanisms responsible for DNA methylation patterns in the tumor immune-infiltrating microenvironment and in cancer immunotherapy remain unclear.

Methods

We conducted a global analysis of the DNA methylation modification pattern (DMP) and immune cell-infiltrating characteristics of LUAD patients based on 21 DNA methylation regulators. A DNA methylation score (DMS) system was constructed to quantify the DMP model in each patient and estimate their potential benefit from immunotherapy.

Results

Two DNA methylation modification patterns able to distinctly characterize the immune microenvironment characterization were identified among 513 LUAD samples. A lower DMS, characterized by increased CTLA-4/PD-1/L1 gene expression, greater methylation modifications, and tumor mutation burden, characterized a noninflamed phenotype with worse survival. A higher DMS, characterized by decreased methylation modification, a greater stromal-relevant response, and immune hyperactivation, characterized an inflamed phenotype with better prognosis. Moreover, a lower DMS indicated an increased mutation load and exhibited a poor immunotherapeutic response in the anti-CTLA-4/PD-1/PD-L1 cohort.

Conclusion

Evaluating the DNA methylation modification pattern of LUAD patients could enhance our understanding of the features of tumor microenvironment characterization and may promote more favorable immunotherapy strategies.

Construction of an immune-related lncRNA pairs model to predict prognosis and immune landscape of lung adenocarcinoma patients

The model of immune-related lncRNA pairs (IRLPs) seems to be an available predictor in lung adenocarcinoma (LUAD) patients. The aim of our study was to construct a model with IRLPs to predict the survival status and immune landscape of LUAD patients. Based on TCGA-LUAD dataset, a risk assessment model with IRLPs was established. Then, ROC curves were used to assess the predictive accuracy and effectiveness of our model. Next, we identified the difference of survival, immune cell infiltration, immune checkpoint inhibitor-related (ICI-related) biomarkers, and chemotherapeutics between high-risk group and low-risk group. Finally, A nomogram was built for predicting the survival rates of LUAD patients. 464 LUAD samples were randomly and equally divided into a training set and a test set. Six IRLPs were screened out to construct a risk model. K-M analysis and risk-plot suggested the prognosis of high-risk group was worse than low-risk group (p < 0.001). Multivariate analysis shows risk score was independent risk factor of LUAD (p < 0.001). In addition, the expression of immune cell infiltration, ICI-related biomarkers, chemotherapeutics all demonstrate significant difference in two groups. A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients. Our immune-related lncRNA pairs risk model is expected to be a reliable model for predicting the prognosis and immune landscape of LUAD patients.