Thanh Dat Nguyen

Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, largely because of challenges in early diagnosis and the limited sensitivity of conventional biomarkers. Therefore, reliable molecular tools for early detection, prognostic stratification, and individualized treatment predictions are urgently required. Methods: This retrospective study analyzed publicly available gene expression datasets. Candidate biomarkers were identified from the GSE14520 cohort using a multistep screening workflow that integrated differential expression analysis, diagnostic performance, and prognostic relevance. A 10-gene diagnostic model was constructed using least absolute shrinkage and selection operator logistic regression and subsequently validated across multiple independent cohorts. Survival outcomes were evaluated using the Kaplan-Meier analysis and treatment responses to sorafenib and transarterial chemoembolization (TACE) were assessed using receiver operating characteristic analysis. Results: A 10-gene signature (TOP2A, CDK1, CYP3A4, MASP2, EPHX2, HAO1, RACGAP1, GLYAT, ADH1B, and CYP4A11) was established. The model demonstrated robust internal performance and consistent accuracy across external validation cohorts (area under the curve [AUC], >0.9). This signature effectively identified early-stage HCC and distinguished malignancy from cirrhosis. High-risk scores were significantly associated with poor overall survival and recurrence-free survival (p < 0.05). Furthermore, the model could predict treatment sensitivity, with higher risk scores associated with better outcomes for sorafenib (AUC, 0.791), whereas lower risk scores correlated with an improved response to TACE (AUC, 0.768). Conclusion: Our gene expression-based machine learning model provides a robust tool for HCC diagnosis, prognosis, and treatment response prediction, with potential as a supportive system for personalized clinical decision-making.

Breast cancer is the most common cancer among women, and metastasis to the lung is associated with poor prognosis. Reliable biomarkers for predicting lung metastasis are urgently needed to improve early detection and clinical decision-making. This study used microarray data sets comprising gene expression profiles and clinical data from primary breast cancer patients who were followed up for lung metastasis outcomes. High-throughput screening combined with Venn diagram analysis was used to identify common candidate probes, and the least absolute shrinkage and selection operator method were used to select 11 genes for model development. Logistic regression was used to construct predictive models, and the final risk signature consisted of 10 candidate genes (CDK19, GLUD1, GTPBP4, HLCS, HYI, KCND3, MAP2K1, NMUR1, PRKD3, and SLC16A3). The model achieved strong performance in training and validation cohorts (areas under the curve > 0.87) and generalized to the independent METABRIC data set (area under the curve = 0.706). Subset analyses restricted to patients with early-stage disease confirmed that the signature retained predictive value. Kaplan-Meier analyses showed that patients with high-risk scores had shorter lung metastasis–free survival, recurrence-free survival, and overall survival. Multivariate Cox analysis confirmed that the risk signature provided independent predictive information from clinical variables. In conclusion, the risk signature accurately identifies patients with breast cancer at risk of lung metastasis, enabling clinicians to better assess risk and tailor effective treatment strategies.

Background: Breast cancer is the most common cancer in women, and the lung is one of the frequent sites for metastasis in breast cancer. Identifying biomarkers to predict lung metastasis is essential for early detection and targeted intervention, thereby improving survival rates for patients with lung metastatic breast cancer. Method: Four datasets (E-MTAB-365, GSE2603, GSE11078, and GSE14020) from NCBI and Array Express databases, containing gene expression profiles and clinical data from breast cancer patients, were selected. High-throughput screening identified potential biological markers by evaluating the predictive ability of each gene for lung metastasis, and a Venn diagram was used to find common genes across datasets with an AUC > 0.65. Using the linear regression algorithm, we developed a gene-based model to predict the risk of lung metastasis with E-MTAB-365 as the training dataset and the remaining datasets for validation. Model performance was evaluated through ROC curve analysis and the Kaplan-Meier curve. Results: The model, consisting of three genes (IRAK1, ATP11A, and LYN), achieved a good AUC across the analyzed datasets. The model demonstrated that patients with lung metastasis had significantly higher risk scores than those without. In addition, patients with high-risk scores faced a higher risk of lung metastasis and a shorter non-metastatic survival time. Conclusion: We successfully built a machine-learning model based on gene expression to predict lung metastasis in breast cancer patients and validated its reliability. Our results suggest the potential application of the established model as a predictive tool to assist physicians in practical diagnosis.

The adverse effects of COVID-19 infections during pregnancy have been extensively documented; however, the persistent sequelae of this infection remain unexplored. This study aimed to investigate the mental health status of women who recovered from COVID-19 infection during pregnancy using the Depression, Anxiety, and Stress Scale (DASS-21) questionnaire. A cross-sectional observational study enrolled 104 women, including 56 participants who recovered from COVID-19 infection during pregnancy (COVID-19 pregnant women-CPW) and 48 controls who were never infected with COVID-19 during pregnancy (non-COVID-19 pregnant women, NCPW) from November 15th, 2022, to February 15th, 2023, at the Respiratory Department in Children’s Hospital 1, Ho Chi Minh City. Data were collected through a questionnaire that gathered general participant information and included a DASS-21 questionnaire. Results showed that 77% of the CPW reported at least one long-term symptom, with fatigue (62.50%), sore throat (60.71%), and cough (57.14%) being the most common. CPW exhibited significantly higher stress, anxiety, and depression scores compared to NCPW (p < 0.0108, p < 0.0006 and p < 0.0007, respectively). Depression correlated significantly with residence (p < 0.0394). Spearman correlation analysis indicated positive associations between depression, anxiety, and stress core in CPW. These findings suggested that COVID-19 infection during pregnancy exerts a notable impact on mental health outcomes, including anxiety, depression, and stress. Further investigations should be performed to elucidate potential mechanisms underlying these findings to develop interventions supporting maternal and infant mental health.