The pathological significance and potential mechanism of ARHGEF6 in lung adenocarcinoma

Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) represents one of the most prevalent forms of lung cancer, with a five-year survival rate of 21.7%. There is an urgent need to identify pertinent biomarkers to inform the diagnosis and prognosis of tumors, particularly those that can be applied to different age groups. Herein, we would apply machine learning methods to specifically analyze the issue of biomarker applicability across different age groups in NSCLC.

METHODS

Studies have shown a higher incidence of NSCLC in people over 40 years of age, and due to the limitations of data set, studies of individuals under 40 years of age were not included in this study. To simulate the human aging model as closely as possible, we gathered corresponding non-small cell lung cancer (NSCLC) samples from the UCSC Xena database based on patient age information. These samples were then categorized into three groups: 40-60, 60-80, and over 80 years old. Subsequently, we employed four machine learning methods-Random Forest, LASSO regression analysis, XGBoost, and GBM-to identify gene sets with significant diagnostic value for each age group. By taking the intersection of these sets, we identified the optimal gene and assessed its prognostic significance in NSCLC. Then, the diagnostic value of CAT gene was validated using global public databases, including the GSE32863, GSE43458, GSE68571, GSE10072, and GSE63459 datasets from the Americas, the GSE30219 and GSE102511 datasets from Europe, and the GSE31210 and GSE19804 datasets from Asia. Furthermore, immunohistochemical staining was performed in an independent cohort from a tissue microarray. Additionally, cell culture and RT-qPCR were employed for external validation.

RESULTS

Through the implementation of machine learning methods, we successfully identified the catalase (CAT) gene. Our analysis revealed that individuals with high expression of the CAT gene experienced improved survival rates. Additionally, these individuals exhibited elevated immune scores. We further discovered that the CAT gene synergizes with multiple components of neutrophils, including TLRs, FcRn, and the selective GEF of Rho-family GTPases. In addition, we identified a potential immune checkpoint, TNFSF15, which is applicable to the human aging model. Finally, we validated the CAT gene's diagnostic value using databases encompassing the Americas, Europe, and Asia regions. Through external RT-qPCR validation, we verified that CAT expression in BEAS-2B was higher than that of A549. In an independent human cohort, we also verified that CAT is lowly expressed in lung cancer tissues. In addition, higher CAT levels were associated with improved survival in the 40-60 and 60-80 age groups.

CONCLUSIONS

In our analysis of the NSCLC database, we pinpointed the CAT gene, which holds promise for potential diagnostic and prognostic applications in the context of human aging. Furthermore, it may offer insights into addressing age-related heterogeneity of NSCLC.

A novel transcriptomic signature associated with lymphovascular invasion predicts clinical outcomes, tumor microenvironment, and therapeutic response in lung adenocarcinoma

PURPOSE

Since TNM staging has limitations for predicting post-operative outcomes and relapse, more effective prediction tools need to be researched and developed. Lymphovascular invasion, LVI, as a histopathological feature, has been widely shown to have a correlation with poor prognosis and early recurrence of lung adenocarcinoma (LUAD). However, LVI assessment is limited by subjective bias, and therefore its efficacy in practical clinical application needs further clarification. The aim of this study was to formulate a new signature based on LVI-related genes to predict prognosis and recurrence in patients with lung adenocarcinoma.

METHODS

Clinicopathological information, gene sequencing data and whole slide images (WSIs) of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA) databases. LVI statue were evaluated by professional pathologists, and then the differentially expressed genes (LVI DEGs) associated with LVI were screened. The least absolute shrinkage and selection operator (LASSO) and Step Cox regression models were used to construct LVI-associated risk scores (LVRS), including PAQR4, ARGHEF6, CKS1B, CFTR and SEC14L4. The validity of the LVRS score was evaluated on multiple external datasets and our JSSZL cohort dataset. Using LVRS scores and clinical information, nomogram were constructed for use by clinicians. In addition, we further explored the relationship between LVRS score and clinicopathological features, immune infiltration, tumor mutational load, and immunotherapy response, and confirmed the expression of key genes in LVRS score in lung adenocarcinoma tissues using qRT-PCR and immunohistochemistry (IHC) techniques.

RESULTS

Based on the LVRS, patients could be classified into high-LVRS and low-LVRS groups. It was found that OS and PFS were significantly worse in the high-LVRS group than in the low-LVRS group (p < 0.001). By ROC curve analysis, it could be found that the nomogram combining LVRS and clinical information could accurately predict the prognosis of LUAD patients with the area under the curve of 1,3,5-year survival rate could reach 0.754, 0.741 and 0.735. The results of univariate and multivariate analysis showed that LVRS was an independent prognostic factor. At the same time, there were significant differences in the mutation profiles and immune microenvironment between the high-LVRS and low-LVRS groups, with the high-LVRS group having a significantly higher mutation rate than the low-LVRS group and exhibiting immunological "cold" features. By the experimental results, higher expression levels of PAQR4 and CKS1B were found in LUAD tissues, while lower expression levels of ARGHEF6, CFTR and SEC14L4 were observed.

CONCLUSIONS

The LVRS established in this study serves as a valid tool to predict the prognosis and recurrence status of lung adenocarcinoma patients and has a predictive effect on the response to postoperative treatment. The establishment of LVRS may offer some theoretical support to clinical treatment strategies for patients with lung adenocarcinoma following surgical intervention.