Radiotherapy prognosis-associated gene GCNT3 promotes the proliferation, migration and invasion of lung adenocarcinoma cells

GCNT3 Promotes Hepatocellular Carcinoma Progression and EMT by Activating the PI3K/AKT Pathway

Primary liver cancer, specifically hepatocellular carcinoma (HCC), is a major global health concern. GCNT3 has been identified as an oncogene in various human malignancies. This investigation aimed to discover the GCNT3 function in HCC. The present study employed integrated bioinformatics analyses to assess the expression pattern, prognostic implications, and putative function of GCNT3 in HCC. Transwell flow cytometry, CCK-8, and wound healing assays were performed to examine HCC cell growth, cell cycle, apoptosis, invasion, and migration. In addition, the epithelial-mesenchymal transition (EMT) markers and PI3K/AKT mechanism markers were examined via western blot analysis to elucidate the underlying mechanisms. In HCC, GCNT3 was significantly overexpressed, which was connected with enhanced tumor aggressiveness and an unfavorable prognosis of individuals. In vitro experiments demonstrated that elevated levels of GCNT3 promoted cell growth, migration, cell cycle development, and invasion, in addition to EMT, while suppressing apoptosis. Conversely, knockdown of GCNT3 exerted the opposite effects. GCNT3 overexpression increased PI3K/AKT phosphorylation in HCC cells, and LY294002 counteracted the impacts of upregulated GCNT3 on cell cycle, migration, invasion, proliferation, and EMT in HCC. The investigation showed that GCNT3 may enhance HCC progression and EMT by stimulating PI3K/AKT mechanism.

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters

Objective

This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model.

Methods

A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell's consistency was analyzed.

Results

Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively.

Conclusion

Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

Integrative bioinformatics analysis reveals ECM and nicotine-related genes in both LUAD and LUSC, but different lung fibrosis-related genes are involved in LUAD and LUSC

There are several bioinformatics studies related to lung cancer, but most of them have mainly focused on either microarray data or RNA-Seq data alone. In this study, we have combined both types of data to identify differentially expressed genes (DEGs) specific to lung cancer subtypes. We obtained six microarray datasets from the GEO and also the expression matrix of LUSC and LUAD from TCGA, which were analyzed by GEO2R tool and GEPIA2, respectively. Enrichment analyses of DEGs were performed using the Enrichr database. Protein module identification was done by MCODE plugin in cytoscape software. We identified 30 LUAD-specific, 17 LUSC-specific, and 17 DEGs shared between LUAD and LUSC. Enrichment analyses revealed that LUSC-specific DEGs are involved in lung fibrosis. In addition, DEGs shared between LUAD and LUSC are involved in extracellular matrix (ECM), nicotine metabolism, and lung fibrosis. We identified lung fibrosis-related genes, including SPP1, MMP9, and CXCL2, involved in both LUAD and LUSC, but SERPINA1 and PLAU genes involved only in LUSC. We also found an important module separately for LUAD-specific, LUSC-specific, and shared DEGs between LUSC and LUAD. S100P, GOLM, AGR2, AK1, TMEM125, SLC2A1, COL1A1, and GHR genes were significantly associated with survival. Our findings suggest that different lung fibrosis-related genes may play roles in LUSC and LUAD. Additionally, nicotine metabolism and ECM remodeling were found to be associated with both LUSC and LUAD, regardless of subtype, emphasizing the role of smoking in the development of lung cancer and ECM in the high aggressiveness and mortality of lung cancer.