Identification of EGFR mutation status in male patients with non-small-cell lung cancer: role of 18F-FDG PET/CT and serum tumor markers CYFRA21-1 and SCC-Ag

Promising predictive molecular biomarkers for cervical cancer (Review)

Cervical cancer (CC) constitutes a serious public health problem. Vaccination and screening programs have notably reduced the incidence of CC worldwide by >80%; however, the mortality rate in low‑income countries remains high. The staging of CC is a determining factor in therapeutic strategies: The clinical management of early stages of CC includes surgery and/or radiotherapy, whereas radiotherapy and/or concurrent chemotherapy are the recommended therapeutic strategies for locally advanced CC. The histopathological characteristics of tumors can effectively serve as prognostic markers of radiotherapy response; however, the efficacy rate of radiotherapy may significantly differ among cancer patients. Failure of radiotherapy is commonly associated with a higher risk of recurrence, persistence and metastasis; therefore, radioresistance remains the most important and unresolved clinical problem. This condition highlights the importance of precision medicine in searching for possible predictive biomarkers to timely identify patients at risk of treatment response failure and provide tailored therapeutic strategies according to genetic and epigenetic characteristics. The present review aimed to summarize the evidence that supports the role of several proteins, methylation markers and non‑coding RNAs as potential predictive biomarkers for CC.

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA

Objective

The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means.

Materials and methods

We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations.

Results

EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency.

Conclusion

CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.

Prognostic significance of integrating total metabolic tumor volume and EGFR mutation status in patients with lung adenocarcinoma

Background

The objective of this study was to investigate the prognostic significance of total metabolic tumor volume (TMTV) derived from baseline 18F-2-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), in conjunction with epidermal growth factor receptor (EGFR) mutation status, among patients with lung adenocarcinoma (LUAD).

Methods

We performed a retrospective analysis on 141 patients with LUAD (74 males, 67 females, median age 67 (range 34-86)) who underwent 18F-FDG PET/CT and had their EGFR mutation status determined. Optimal cutoff points for TMTV were determined using time-dependent receiver operating characteristic curve analysis. The survival difference was compared using Cox regression analysis and Kaplan‒Meier curves.

Results

The EGFR mutant patients (n = 79, 56.0%) exhibited significantly higher 2-year progression-free survival (PFS) and overall survival (OS) rates compared to those with EGFR wild-type (n = 62, 44.0%), with rates of 74.2% vs 69.2% (P = 0.029) and 86.1% vs 67.7% (P = 0.009), respectively. The optimal cutoff values of TMTV were 36.42 cm3 for PFS and 37.51 cm3 for OS. Patients with high TMTV exhibited significantly inferior 2-year PFS and OS, with rates of 22.4% and 38.1%, respectively, compared to those with low TMTV, who had rates of 85.8% and 95.0% (both P < 0.001). In both the EGFR mutant and wild-type groups, patients exhibiting high TMTV demonstrated significantly inferior 2-year PFS and OS compared to those with low TMTV. In multivariate analysis, EGFR mutation status (hazard ratio, HR, 0.41, 95% confidence interval, CI [0.18-0.94], P = 0.034) and TMTV (HR 8.08, 95% CI [2.34-28.0], P < 0.001) were independent prognostic factors of OS, whereas TMTV was also an independent prognosticator of PFS (HR 2.59, 95% CI [1.30-5.13], P = 0.007).

Conclusion

Our study demonstrates that the integration of TMTV on baseline 18F-FDG PET/CT with EGFR mutation status improves the accuracy of prognostic evaluation for patients with LUAD.

Serum THBS2 is a potential biomarker for the diagnosis of non-small cell lung cancer

OBJECTIVE

This study primarily aimed to analyze the levels of THBS2 in the serum of patients diagnosed with non-small cell lung cancer (NSCLC), and subsequently evaluate its potential as a diagnostic biomarker for NSCLC.

METHODS

Serum samples were collected from 150 diagnosed NSCLC patients and 150 healthy individuals. The THBS2 concentration in these samples was determined using an enzyme-linked immunosorbent assay (ELISA). The study also investigated the correlation between THBS2 levels and various clinicopathological characteristics in NSCLC patients. The diagnostic sensitivity and specificity of serum THBS2 for NSCLC were assessed using receiver operating characteristic (ROC) curves and their corresponding area under the curve (AUC).

RESULTS

Serum THBS2 levels in NSCLC patients were significantly elevated compared to those in healthy individuals. THBS2 levels showed a significant correlation with tumor differentiation grade, tumor size, TNM stage, lymph node metastasis, and distant metastasis. No significant correlation was identified between serum THBS2 levels and other parameters such as gender, age, height, weight, BMI, smoking history, and tumor histological type. At a cutoff value of 7.62 ng/mL, THBS2 could effectively differentiate NSCLC patients from healthy individuals, with a sensitivity of 85.31% and a specificity of 88.92%. The AUC for NSCLC diagnosis using THBS2 was 0.812, significantly surpassing the performance of traditional tumor markers tested, including CEA (0.728), and CYFRA 21‑1 (0.685).

CONCLUSIONS

Elevated serum THBS2 levels in NSCLC patients suggest its potential as a novel and reliable diagnostic biomarker for NSCLC. Its superior diagnostic performance could potentially outperform traditional tumor markers, leading to improved patient outcomes.

Association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer

Background

Cigarette smoking exerts a significant impact on metabolic phenotypes and epidermal growth factor receptor (EGFR) mutation status; however, their correlation remains insufficiently established. Therefore, the aim of this study was to investigate the association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

Methods

We retrospectively analyzed 198 consecutive patients with NSCLC who underwent 18F-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) before treatment and were tested for EGFR mutation status between September 2019 and March 2022. Metabolic phenotypes, including the maximum standardized uptake value (SUVmax) of the primary tumors (pSUVmax), metastatic lymph nodes (nSUVmax), and distant metastases (mSUVmax) were assessed. Patients were classified into never-smokers and smokers based on detailed smoking history. The correlations between smoking status, metabolic parameters, and EGFR mutation status were evaluated in patients with NSCLC.

Results

We observed EGFR mutations in 73 (60.3%) of 121 never-smokers and 18 (23.4%) of 77 smokers (P<0.001). EGFR-mutant NSCLC had a lower pSUVmax than that of EGFR wild-type (WT; 8.9±4.5 vs. 12.7±6.9, P<0.001). Smokers had a higher pSUVmax than never-smokers (12.5±6.4 vs. 9.9±5.9, P=0.004). With the increase of cumulative smoking dose, the pSUVmax increased significantly (r=0.198, P=0.005). There was no significant difference between nSUVmax and mSUVmax in patients with or without EGFR mutation and smoking history. Cumulative smoking dose, pSUVmax, and their combination predicted EGFR mutation status with areas under the receiver operating characteristic (ROC) curves (AUCs) 0.688, 0.673, and 0.753, respectively.

Conclusions

Our findings indicate that cigarette smoking may be one of the triggers for increased pSUVmax and decreased EGFR mutations, further suggesting that EGFR mutations are associated with low pSUVmax, which may guide clinicians in risk stratification and treatment strategy selection for patients with NSCLC.