Measurement of Serum EGFR mRNA Expression is a Reliable Predictor of Treatment Response and Survival Outcomes in Non- Small Cell Lung Cancer

Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset

BACKGROUND

Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (MiRNAs) promote cancer cell growth, infiltration, and metastasis. However, the relationship between the miRNA expression profiles and BM occurrence in patients with LUAD remains unclear.

METHODS

We conducted an analysis to identify miRNAs in tissue samples that exhibited different expression levels between patients with and without BM. Using a machine learning approach, we confirmed whether the miRNA profile could be a predictive tool for BM. We performed pathway analysis of miRNA target genes using a matched mRNA dataset.

RESULTS

We selected 25 miRNAs that consistently exhibited differential expression between the two groups of 32 samples. The 25-miRNA profile demonstrated a strong predictive potential for BM in both Group 1 and Group 2 and the entire dataset (area under the curve [AUC] = 0.918, accuracy = 0.875 in Group 1; AUC = 0.867, accuracy = 0.781 in Group 2; and AUC = 0.908, accuracy = 0.875 in the entire group). Patients predicted to have BM, based on the 25-miRNA profile, had lower survival rates. Target gene analysis of miRNAs suggested that BM could be induced through the ErbB signaling pathway, proteoglycans in cancer, and the focal adhesion pathway. Furthermore, patients predicted to have BM based on the 25-miRNA profile exhibited higher expression of the epithelial-mesenchymal transition signature, TWIST, and vimentin than those not predicted to have BM. Specifically, there was a correlation between EGFR mRNA levels and BM.

CONCLUSIONS

This 25-miRNA profile may serve as a biomarker for predicting BM in patients with LUAD.

The First Evaluation of Serum Levels of MGP, Gas6 and EGFR after First Dose of Chemotherapy in Lung Cancer

Simple Summary

Serum levels of MGP, Gas6, vitamin K₁, and EGFR were not significantly changed in response to the first cycle of chemotherapy. We found a strong correlation between MGP and VitK₁ serum values, and a moderate negative correlation between VitK₁ and EGFR in pre-treatment patients. The post-treatment value of EGFR is a strong independent factor that correlates positively with the Gas6 post-treatment values.

Abstract

Background: Vitamin K-dependent proteins (VKDPs) and the epidermal growth factor receptor (EGFR) are involved in lung cancer progression. Therefore, we aimed to study the serum concentration of Matrix Gla protein (MGP), Growth Arrest-specific 6 (Gas6), and EGFR before and after the first cycle of chemotherapy and to investigate how MGP, Gas6, and EGFR are modified after one cycle of chemotherapy. Methods: We performed an observational study on twenty patients diagnosed with lung cancer, by assessing the serum concentration of vitaminK₁ (VitK₁), MGP, Gas6, and EGFR using the ELISA technique before and after three weeks of the first cycle of chemotherapy. Patients were evaluated using RECIST 1.1 criteria. Results: Serum levels of MGP, Gas6, EGFR, and VK₁ before and after treatment were not changed significantly. Regarding the pre-treatment correlation of the MGP values, we found a strong positive relationship between MGP and VK₁ pre-treatment values (r = 0.821, 95%CI 0.523; 0.954, p < 0.001). Furthermore, there was a moderately negative correlation between VK₁ and EGFR pre-treatment values, with the relationship between them being marginally significant (r = −0.430, 95%CI −0.772; 0.001, p = 0.058). Post-treatment, we found a strong positive relationship between MGP and VK₁ post-treatment values (r = 0.758, 95%CI 0.436; 0.900, p < 0.001). We also found a moderate positive relationship between Gas6 and EGFR post-treatment values, but the correlation was only marginally significant (r = 0.442, p = 0.051).

Cell Behavior of Non-Small Cell Lung Cancer Is at EGFR and MicroRNAs Hands

Lung cancer is a complex disease associated with gene mutations, particularly mutations of Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) and epidermal growth factor receptor (EGFR). Non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) are the two major types of lung cancer. The former includes most lung cancers (85%) and are commonly associated with EGFR mutations. Several EGFR-tyrosine kinase inhibitors (EGFR-TKIs), including erlotinib, gefitinib, and osimertinib, are effective therapeutic agents in EGFR-mutated NSCLC. However, their effectiveness is limited by the development (acquired) or presence of intrinsic drug resistance. MicroRNAs (miRNAs) are key gene regulators that play a profound role in the development and outcomes for NSCLC via their role as oncogenes or oncosuppressors. The regulatory role of miRNA-dependent EGFR crosstalk depends on EGFR signaling pathway, including Rat Sarcoma/Rapidly Accelerated Fibrosarcoma/Mitogen-Activated Protein Kinase/Extracellular Signal-Regulated Kinase 1/2 (Ras/Raf/MEK/ERK1/2), Signal Transducer and Activator of Transcription (STAT), Nuclear Factor Kappa-Light-Chain-Enhancer of Activated B Cells (NF-kB), phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), Janus kinase 1 (JAK1), and growth factor receptor-bound protein 2 (GRB2). Dysregulated expression of miRNAs affects sensitivity to treatment with EGFR-TKIs. Thus, abnormalities in miRNA-dependent EGFR crosstalk can be used as diagnostic and prognostic markers, as well as therapeutic targets in NSCLC. In this review, we present an overview of miRNA-dependent EGFR expression regulation, which modulates the behavior and progression of NSCLC.