To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC). 219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model. In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model's predictions were associated with early recurrence and progression-free survival in HCC patients. The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.

Background: Kidney renal clear cell carcinoma (KIRC) is an inflammation-related carcinoma, and inflammation has been recognized as an important factor in inducing carcinogenesis. To further explore the role of inflammation in KIRC, we developed an inflammation-related signature and verified its correlation with the tumor micro-environment.Methods: After the differential inflammation-related prognostic genes were screened by Lasso regression, the inflammation-related signature (IRS) was constructed based on the risk score of multivariate Cox regression. Then, the prognostic value of the IRS was evaluated by Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis and multivariate Cox regression. Gene set variation analysis (GSVA) was applied to screen out enriched signaling pathways. Infiltrated immune cells, tumor mutational burden (TMB) and immune checkpoints were explored by CIBERSORTx and maftool.Results: Four genes (TIMP1, PLAUR, CCL22, and IL15RA) were used to construct the IRS in patients with KIRC. Kaplan-Meier analysis and multivariate Cox regression identified that the IRS could independently predict the prognosis of patients with KIRC in the training and validation groups. The diagnostic value of the nomogram increased from 0.811 to 0.845 after adding the IRS to the multiparameter ROC analysis. The GSVA results indicated that IRS was closely related to primary immunodeficiency and antigen processing and presentation. The immune checkpoint LAG3 was highly expressed in patients with high-risk score (p < 0.05), while CD274 (PD-L1) and HAVCR2 were highly expressed in patients with low-risk score (p < 0.001). There was a significant positive correlation between the high-risk score group and CD8⁺ T, activated CD4⁺ memory T, gamma and delta regulatory T and M0 macrophage cells, while the low-risk score group was negatively associated with B memory, plasma, resting CD4⁺ memory T, activated NK, M1 macrophages and resting mast cells.Conclusion: We found that the IRS might serve as a biomarker to predict the survival of KIRC. Moreover, patients with high or low-risk score might be sensitive to immune drugs at different immune checkpoints.

Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients. A retrospective analysis of 160 gastric cancer patients undergoing radical surgery with continuous follow-up was conducted. Pathomic parameters of the primary lesion and surrounding area were assessed. Six machine learning methods were employed to develop an optimal pathomics model and calculate the pathomics score (Pathscore). The Pathscore was integrated with clinical parameters to construct a comprehensive nomogram for accurate prognosis prediction. The TCGA and GEO databases were utilized for model validation and underlying mechanism exploration. The combined GBM-based pathomics model exhibited superior predictive performance, with 1-, 3-, and 5-year area under the curve (AUC) values of 0.837, 0.970, and 0.963, respectively, in the training set. The pathomics model demonstrated significant incremental value for clinical predictors. The nomogram incorporating age, M stage, TNM stage, and Pathscore achieved even higher AUCs of 0.954, 0.939, and 0.898 for 1-, 3-, and 5-year OS prediction, respectively. Bioinformatics analysis suggested that the pathomics model could predict OS by reflecting tumor immune status and NRP1 expression. Machine learning-based pathomics models can effectively predict OS in gastric cancer patients post-surgery. The integration of pathomics and clinical parameters enhances predictive accuracy compared to clinical factors alone, providing a reliable basis for prognostic prediction and personalized treatment decisions.

KDF1 has been reported to be correlated with carcinogenesis. However, its role and mechanism are far from clear. To explore the possible role and underlying mechanism of KDF1 in lung adenocarcinoma (LUAD), we investigated KDF1 expression in LUAD tissues and the influence of KDF1 in the phenotype of LUAD cells (A549 and PC-9) as well as the underlying mechanism. Compared to non-tumor lung epithelial cells, KDF1 was upregulated in the cancer cells of the majority of LUAD patients, and its expression was correlated with tumor size. Patients with enhanced KDF1 in cancer cells (compared with paired adjacent non-neoplastic lung epithelial cells) had shorter overall survival than patients with no increased KDF1 in cancer cells. Knockdown of KDF1 inhibited the migration, proliferation and invasion of LUAD cells in vitro. And overexpression of KDF1 increased the growth of the subcutaneous tumors in mice. In terms of molecular mechanisms, overexpression of KDF1 induced the expression of AKT, p-AKT and p-STAT3. In KDF1-overexpressing A549 cells, inhibition of the STAT3 pathway decreased the level of AKT and p-AKT, whereas inhibition of the AKT pathway had no effect on the activation of STAT3. Inhibition of STAT3 or AKT pathways reversed the promoting effects of KDF1 overexpression on the LUAD cell phenotype and STAT3 inhibition appeared to have a better effect. Finally, in the cancer cells of LUAD tumor samples, the KDF1 level was observed to correlate positively with the level of p-STAT3. All these findings suggest that KDF1, which activates STAT3 and the downstream AKT pathway in LUAD, acts as a tumor-promoting factor and may represent a therapeutic target.

During ribosome biogenesis, the small subunit (SSU) processome is responsible for 40S assembly. The BMS1/RCL1 complex is a core component of the SSU processome that plays an important role in 18S rRNA processing and maturation. Genetic studies using zebrafish mutants indicate that both Bms1-like (Bms1l) and Rcl1 are essential for digestive organ development. In spite of vital functions of this complex, the mutual dependence of these two nucleolar proteins for the stability and function remains elusive. In this study, we identified an RCL1-interacting domain in BMS1, which is conserved in zebrafish and humans. Moreover, both the protein stability and nucleolar entry of RCL1 depend on its interaction with BMS1, otherwise RCL1 degraded through the ubiquitination–proteasome pathway. Functional studies revealed that overexpression of RCL1 in BMS1-knockdown cells can partially rescue the defects in 18S rRNA processing and cell proliferation, and hepatocyte-specific overexpression of Rcl1 can resume zebrafish liver development in the bms1l substitution mutant bms1l^sq163/sq163but not in the knockout mutant bms1l^zju1/zju1, which is attributed to the nucleolar entry of Rcl1 in the former mutant. Our data demonstrate that BMS1 and RCL1 interaction is essential for not only pre-rRNA processing but also the communication between ribosome biogenesis and cell cycle regulation.