CD63 + tumor-associated macrophages drive the progression of hepatocellular carcinoma through the induction of epithelial-mesenchymal transition and lipid reprogramming.

BACKGROUND: Tumor-associated macrophages (TAMs) constitute a substantial part of human hepatocellular carcinoma (HCC). The present study was devised to explore TAM diversity and their roles in HCC progression. METHODS: Through the integration of multiple 10 × single-cell transcriptomic data derived from HCC samples and the use of consensus nonnegative matrix factorization (an unsupervised clustering algorithm), TAM molecular subtypes and expression programs were evaluated in detail. The roles played by these TAM subtypes in HCC were further probed through pseudotime, enrichment, and intercellular communication analyses. Lastly, vitro experiments were performed to validate the relationship between CD63, which is an inflammatory TAM expression program marker, and tumor cell lines. RESULTS: We found that the inflammatory expression program in TAMs had a more obvious interaction with HCC cells, and CD63, as a marker gene of the inflammatory expression program, was associated with poor prognosis of HCC patients. Both bulk RNA-seq and vitro experiments confirmed that higher TAM CD63 expression was associated with the growth of HCC cells as well as their epithelial-mesenchymal transition, metastasis, invasion, and the reprogramming of lipid metabolism. CONCLUSIONS: These analyses revealed that the TAM inflammatory expression program in HCC is closely associated with malignant tumor cells, with the hub gene CD63 thus representing an ideal target for therapeutic intervention in this cancer type.

Fibroblast Growth Factors for Nonalcoholic Fatty Liver Disease: Opportunities and Challenges.

Nonalcoholic fatty liver disease (NAFLD), a chronic condition associated with metabolic dysfunction and obesity, has reached epidemic proportions worldwide. Although early NAFLD can be treated with lifestyle changes, the treatment of advanced liver pathology, such as nonalcoholic steatohepatitis (NASH), remains a challenge. There are currently no FDA-approved drugs for NAFLD. Fibroblast growth factors (FGFs) play essential roles in lipid and carbohydrate metabolism and have recently emerged as promising therapeutic agents for metabolic diseases. Among them, endocrine members (FGF19 and FGF21) and classical members (FGF1 and FGF4) are key regulators of energy metabolism. FGF-based therapies have shown therapeutic benefits in patients with NAFLD, and substantial progress has recently been made in clinical trials. These FGF analogs are effective in alleviating steatosis, liver inflammation, and fibrosis. In this review, we describe the biology of four metabolism-related FGFs (FGF19, FGF21, FGF1, and FGF4) and their basic action mechanisms, and then summarize recent advances in the biopharmaceutical development of FGF-based therapies for patients with NAFLD.

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection.

RATIONALE AND OBJECTIVES: The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resection. This study develops and validates a deep learning (DL) prediction model to distinguish between proliferative and non-proliferative HCCs using dynamic contrast-enhanced MRI (DCE-MRI), aiming to refine preoperative assessments and optimize treatment strategies by assessing early recurrence risk. MATERIALS AND METHODS: In this retrospective study, 355 HCC patients from two Chinese medical centers (April 2018-February 2023) who underwent radical resection were included. Patient data were collected from medical records, imaging databases, and pathology reports. The cohort was divided into a training set (n = 251), an internal test set (n = 62), and external test sets (n = 42). A DL model was developed using DCE-MRI images of primary tumors. Clinical and radiological models were generated from their respective features, and fusion strategies were employed for combined model development. The discriminative abilities of the clinical, radiological, DL, and combined models were extensively analyzed. The performances of these models were evaluated against pathological diagnoses, with independent and fusion DL-based models validated for clinical utility in predicting early recurrence. RESULTS: The DL model, using DCE-MRI, outperformed clinical and radiological feature-based models in predicting proliferative HCC. The area under the curve (AUC) for the DL model was 0.98, 0.89, and 0.83 in the training, internal validation, and external validation sets, respectively. The AUCs for the combined DL and clinical feature models were 0.99, 0.86, and 0.83 in these sets, while the AUCs for the combined DL, clinical, and radiological model were 0.99, 0.87, and 0.8, respectively. Among models predicting early recurrence, the DL plus clinical features model showed superior performance. CONCLUSION: The DL-based DCE-MRI model demonstrated robust performance in predicting proliferative HCC and stratifying patient risk for early postoperative recurrence. As a non-invasive tool, it shows promise in enhancing decision-making for individualized HCC management strategies.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases.

Background and objective: For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions. Methods: In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS). Results: For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis). Conclusions: This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

High Expression of TGF-ß1 Contributes to Hepatocellular Carcinoma Prognosis via Regulating Tumor Immunity.

Background: Transforming growth factor-beta (TGF-ß) signaling is essential in initialization and progression of hepatocellular carcinoma (HCC). Therefore, a treatment targeting TGF-ß pathway may be a promising option for HCC control. Methods: First, publicly available RNA-seq datasets and clinical characteristics of 374 HCC patients in The Cancer Genome Atlas (TCGA) database were downloaded. Then, Cox regression analysis and LASSO analysis were used to construct a prognostic model for TGF-ß family genes. The area under the curve (AUC) of the risk signature was calculated to evaluate the predictive power of the model. Cox regression analysis was applied to predict whether TGF-ß1 can be an independent prognosis factor for HCC. Next, hazard ratio and survival analyses were performed to investigate the correlation between TGF-ß1 expression and survival time. Furthermore, differential expression level of TGF-ß1 in HCC tissues and cells was determined. In addition, Gene Set Enrichment Analysis (GSEA) identified the top significantly activated and inhibited signal pathways related to high expression of TGF-ß1. Finally, the CIBERSORT tool was adopted to correlate the tumor-infiltrating immune cells (TICs) with TGF-ß1 expression in HCC cohorts. Results: Cox regression analysis and LASSO analysis revealed that seven TGF-ß family members (including TGF-ß1) could be used as prognostic factors for HCC. Interestingly, TGF-ß1 was demonstrated to be an independent prognostic factor of HCC. RT-qPCR and immunofluorescence staining confirmed the high expression of TGF-ß1 in HCC cell lines and tissues, which is significantly related to pathological classifications, poor prognosis, and short survival time. Finally, GSEA and CIBERSORT analyses suggested that TGF-ß1 may interact with various immune cells and influence the prognosis of HCC patients through Tregs and γδ T cells. Conclusion: We established a novel prognostic prediction method to predict the risk scores of TGF-ß genes in HCC prognosis. TGF-ß1 is highly expressed in HCC cell lines and tissues, correlates to poor prognosis, and thus can be used as a potential biomarker to predict HCC prognosis. We showed that TGF-ß1 may play its roles in HCC prognosis by modulating the immune microenvironment of tumor cells. Our data may shed more light on better understanding the role of TGF-ß1 in HCC prognosis.

Novel genotypes of Cryptosporidium and Enterocytozoon bieneusi detected in plateau zokors (Myospalax baileyi) from the Tibetan Plateau.

The plateau zokor (Myospalax baileyi) is a small subterranean rodent endemic to China that lives alone in sealed underground burrows at altitudes ranging from 2000 to 4200 m above sea level on the Tibetan Plateau. Due to the unique environmental factors in the Tibetan Plateau, intestinal parasites in the local population may be more likely to develop host-adapted genotypes. We therefore conducted an epidemiological survey of common intestinal parasites in plateau zokors on the Tibetan plateau to estimate their actual gastrointestinal parasite status. Two areas with high populations of plateau zokor in Xunhua County, Qinghai Province were selected as sampling sites, and a total of 98 zokors were trapped. Four parasites, Cryptosporidium spp., Enterocytozoon bieneusi, Giardia lamblia and Blastocystis hominis, were tested in the faecal samples. The results showed that a new genotype of Cryptosporidium sp. was identified by amplification and sequencing of a portion of the small subunit ribosomal RNA (SSU rRNA) gene with an infection rate of 1.0% (1/98), and new genotypes of E. bieneusi were identified by amplification and sequencing of a portion of the internal transcribed spacer (ITS) region of the ribosomal RNA gene sequences with an infection rate of 4.1% (4/98). Neither of the two intestinal parasites, G. lamblia and B. hominis, was detected.

NR2F1-AS1/miR-190a/PHLDB2 Induces the Epithelial-Mesenchymal Transformation Process in Gastric Cancer by Promoting Phosphorylation of AKT3.

The median survival time of patients with advanced gastric cancer (GC) who received radiotherapy and chemotherapy was <1 year. Epithelial-mesenchymal transformation (EMT) gives GC cells the ability to invade, which is an essential biological mechanism in the progression of GC. The long non-coding RNA (lncRNA)-based competitive endogenous RNA (ceRNA) system has been shown to play a key role in the GC-related EMT process. Although the AKT pathway is essential for EMT in GC, the relationship between AKT3 subtypes and EMT in GC is unclear. Here, we evaluated the underlying mechanism of ceRNA involving NR2F1-AS1/miR-190a/PHLDB2 in inducing EMT by promoting the expression and phosphorylation of AKT3. The results of bioinformatics analysis showed that the expression of NR2F1-AS1/miR-190a/PHLDB2 in GC was positively associated with the pathological features, staging, poor prognosis, and EMT process. We performed cell transfection, qRT-PCR, western blot, cell viability assay, TUNEL assay, Transwell assay, cell morphology observation, and double luciferase assay to confirm the regulation of NR2F1-AS1/miR-190a/PHLDB2 and its effect on EMT transformation. Finally, GSEA and GO/KEGG enrichment analysis identified that PI3K/AKT pathway was positively correlated to NR2F1-AS1/miR-190a/PHLDB2 expression. AKT3 knockout cells were co-transfected with PHLDB2-OE, and the findings revealed that AKT3 expression and phosphorylation were essential for the PHLDB2-mediated EMT process. Thus, our results showed that NR2F1-AS1/miR-190a/PHLDB2 promoted the phosphorylation of AKT3 to induce EMT in GC cells. This study provides a comprehensive understanding of the underlying mechanism involved in the EMT process as well as the identification of new EMT markers.

Development and Validation of a Prognostic Nomogram for Gastric Signet Ring Cell Carcinoma: A Multicenter Population-Based Study.

BACKGROUND: Gastric signet ring cell carcinoma (GSRCC) is a rare disease associated with poor prognosis. A prognostic nomogram was developed and validated in this study to assess GSRCC patients' overall survival (OS). METHODS: Patients diagnosed with GSRCC from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2016) and the First Hospital of China Medical University (CMU1h) were enrolled in this retrospective cohort study. Univariate and multivariate COX analysis was used to determine independent prognostic factors to construct the prognostic nomogram. Predictions were evaluated by the C-index and calibration curve. In addition, the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and Kaplan-Meier analysis were employed to assess the clinical utility of the survival prediction model. RESULTS: Patients were classified into two cohorts. We randomly divided patients in the SEER database and CMU1h cohort into a training group (n=3068, 80%) and a validation group (n=764, 20%). Age, race, T stage, N stage, M stage, therapy, and tumor size were significantly associated with the prognosis of GSRCC patients. On this basis, a nomogram was constructed, with a C-index in the training and the validation cohorts at 0.772 (95% CI: 0.762-0.782) and 0.774 (95% CI: 0.752-0.796), respectively. The accuracy of the generated nomogram was verified through calibration plots. Similarly, compared with the traditional AJCC staging system, the results of the area under curve (AUC) calculated by ROC, DCA, and Kaplan-Meier curves, demonstrated a good predictive value of the constructed nomogram, compared to the traditional AJCC staging system. CONCLUSION: In the present study, seven independent prognostic factors of GSRCC were screened out. The established nomogram models based on seven variables provided a visualization of each prognostic factor's risk and assisted clinicians in predicting the 1-, 3-, and 5-year OS of GSRCC.

Construction of an immune-related gene signature to predict survival and treatment outcome in gastric cancer.

Immune cells have emerged as key regulators in the occurrence and development of multiple tumor types. However, it is unclear whether immune-related genes (IRGs) and the tumor immune microenvironment can predict prognosis for patients with gastric cancer (GC). The mRNA expression data in GC tissues (n = 368) were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs in patients with GC were determined using a computational difference algorithm. A prognostic signature was constructed using COX regression and random survival forest (RSF) analyses. In addition, datasets related to "gemcitabine resistance" and "trastuzumab resistance" (GSE58118 and GSE77346) were obtained for GEO database, and DEGs associated with drug-resistance were screened. Then, we analyzed correlations between gene expression and cancer immune infiltrates via Tumor Immune Estimation Resource (TIMER) site. The cBioportal database was used to analyze drug-resistant gene mutation status and survival. One hundred and fifty-five differentially expressed IRGs were screened between GC and normal tissues, and a prognostic signature consisting of four IRGs (NRP1, PPP3R1, IL17RA, and FGF16) was closely related to the overall survival (OS). According to cutoff value of risk score, patients were divided into high-risk and low-risk group. Patients in the high-risk group had shorter OS compared to the low-risk group in both the training (p < 0.0001) and testing sets (p = 0.0021). In addition, we developed a 5-IRGs (LGR6, DKK1, TNFRSF1B, NRP1, and CXCR4) signature which may participate in drug resistance processes in GC. Survival analysis showed that patients with drug-resistant gene mutations had shorter OS (p = 0.0459) and DFS (p < 0.001). We constructed four survival-related IRGs and five IRGs related to drug resistance which may contribute to predict the prognosis of GC.