Hepatocellular carcinoma (HCC) is one of the leading contributors to cancer-related mortality worldwide. Nop2/Sun domain family member 5 (NSUN5), a conserved RNA 5-methylcytosine methyltransferase, is conventionally recognized as oncogenic. However, its role in HCC development remains unknown. In this study, we observed a remarkable upregulation of NSUN5 expression in both tumor tissues from patients with HCC, establishing a correlation with unfavorable clinical outcomes. NSUN5 knockdown and overexpression significantly inhibited and promoted HCC cell proliferation, respectively. Additionally, employing a combination of methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RIP-seq techniques, we identified zinc finger BED domain-containing protein 3 (ZBED3) as a novel downstream target of NSUN5. Additionally, we found that the overexpression of ZBED3 counteracted the tumor-suppressing effect of NSUN5 knockdown and simultaneously reversed the inhibition of the Wnt/β-catenin signaling pathway. In summary, we elucidated the oncogenic role of NSUN5 in HCC development and identified the ZBED3/Wnt/β-catenin signaling pathway as its downstream target. This study provides a novel therapeutic target for further development in HCC treatment.

Background: The occurrence of epithelial-mesenchymal transition (EMT) and immune evasion is considered to contribute to poor prognosis in lung adenocarcinoma (LUAD). Therefore, this study aims to explore the key oncogenes that promote EMT and immune evasion and reveal the expression patterns, prognostic value, and potential biological functions. Methods: Firstly, we identified gene modules associated with EMT and Tumor Immune Dysfunction and Exclusion (TIDE) through weighted gene co-expression network analysis (WGCNA). Next, we utilized differential analysis and machine learning to identify the key genes and validate them. Moreover, we analyzed the correlation between key genes and tumor microenvironment remodeling, drug sensitivity, as well as mutation frequency. Furthermore, we explored and validated their malignant biological characteristics through in vitro experiments and clinical samples. Finally, potential drugs for LUAD were screened based on CMap and validated through experiments. Results: Firstly, WGCNA analysis revealed that red and green modules were highly correlated with EMT and TIDE. Among them, upregulated expression of SPOCK1 was observed in lung adenocarcinoma tissues and was associated with poor prognosis. Additionally, patients in the high SPOCK1 group showed more activation of malignant oncogenic pathways, higher infiltration of immunosuppressive components, and a higher frequency of mutations. The knockdown of SPOCK1 suppressed invasion and metastasis capabilities of lung adenocarcinoma cells, and the high expression of SPOCK1 was associated with low infiltration of CD8⁺ T cells. Therapeutic aspects, SPOCK1 can be a candidate indicator for drug sensitivity and CMap showed that VER-155008 was the drug candidate with the largest perturbation effect on the SPOCK1 expression profile. In vitro and in vivo experiments validated the cancer-inhibitory effect of VER-155008 in LUAD. Conclusion: This study revealed through comprehensive bioinformatics analysis and experimental analysis that SPOCK1 can promote EMT and immune escape in LUAD, and it may serve as a promising candidate prognostic biomarker and therapeutic target for LUAD.

Objective: To investigate the relationship between the neutrophil-to-lymphocyte ratio (NLR) of patients with non-small cell lung cancer (NSCLC) and their risk of developing brain metastases after adjusting for confounding factors. Methods: A retrospective observational study of the general data of patients with NSCLC diagnosed from January 2016 to December 2020. Multivariate logistic regression was used to calculate the dominance ratio (OR) with 95% confidence interval (CI) for NLR and NSCLC brain metastases with subgroup analysis. Generalized summation models and smoothed curve fitting were used to identify whether there was a nonlinear relationship between them. Results: In all 3 models, NLR levels were positively correlated with NSCLC brain metastasis (model 1: OR: 1.12, 95% CI: 1.01-1.23, P = .025; model 2: OR: 1.16, 95% CI: 1.04-1.29, P = .007; model 3: OR: 1.20, 95% CI: 1.05-1.37, P = .006). Stratified analysis showed that this positive correlation was present in patients with adenocarcinoma (LUAD) and female patients (LUAD: OR: 1.30, 95% CI: 1.10-1.54, P = .002; female: OR: 1.52, 95% CI: 1.05-2.20, P = .026), while there was no significant correlation in patients with squamous carcinoma (LUSC) and male patients (LUSC: OR:0.76,95% CI:0.38- 1.53, P = .443; male: OR:1.13, 95% CI:0.95-1.33, P = .159). Conclusion: This study showed that elevated levels of NLR were independently associated with an increased risk of developing brain metastases in patients with NSCLC, and that this correlation varied by TYPE and SEX, with a significant correlation in female patients and patients with LUAD.

Objective: To develop and validate a generalized prediction model that can classify epidermal growth factor receptor (EGFR) mutation status in non–small cell lung cancer patients. Methods: A total of 346 patients (296 in the training cohort and 50 in the validation cohort) from four centers were included in this retrospective study. First, 1085 features were extracted using IBEX from the computed tomography images. The features were screened using the intraclass correlation coefficient, hypothesis tests and least absolute shrinkage and selection operator. Logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) were used to build a radiomics model for classification. The models were evaluated using the following metrics: area under the curve (AUC), calibration curve (CAL), decision curve analysis (DCA), concordance index (C-index), and Brier score. Results: Sixteen features were selected, and models were built using LR, DT, RF, and SVM. In the training cohort, the AUCs was .723, .842, .995, and .883; In the validation cohort, the AUCs were .658, 0567, .88, and .765. RF model with the best AUC, its CAL, C-index (training cohort=.998; validation cohort=.883), and Brier score (training cohort=.007; validation cohort=0.137) showed a satisfactory predictive accuracy; DCA indicated that the RF model has better clinical application value. Conclusion: Machine learning models based on computed tomography images can be used to evaluate EGFR status in patients with non–small cell lung cancer, and the RF model outperformed LR, DT, and SVM.

Cancer is one of the leading causes of death worldwide. The burden of cancer on public health is becoming more widely acknowledged. Lung cancer has one of the highest incidence and mortality rates of all cancers. The prevalence of early screening, the emergence of targeted therapy, and the development of immunotherapy have all significantly improved the overall prognosis of lung cancer patients. The current state of affairs, however, is not encouraging, and there are issues like poor treatment outcomes for some patients and extremely poor prognoses for those with advanced lung cancer. Because of their potent immunomodulatory capabilities, thymosin drugs are frequently used in the treatment of tumors. The effectiveness of thymosin drugs in the treatment of lung cancer has been demonstrated in numerous studies, which amply demonstrates the potential and future of thymosin drugs for the treatment of lung cancer. The clinical research on thymosin peptide drugs in lung cancer and the basic research on the mechanism of thymosin drugs in anti-lung cancer are both systematically summarized and analyzed in this paper, along with future research directions.