Knockdown of SLC16A3 decreases extracellular lactate concentration in hepatocellular carcinoma, alleviates hypoxia and induces ferroptosis.

SLC16A3/monocarboxylate transporter 4 (MCT4) regulates intracellular lactate transport and is highly expressed in many tumors, indicating poor prognosis. It may be related to inducing hypoxia, apoptosis and other mechanisms, but the study of MCT4 in HCC is far from complete. In this study, we first analyzed the expression of SLC16A3 in HCC tumor and non-tumor tissue samples based on TCGA data and immunohistochemistry. Subsequently, the effects of SLC16A3 expression on cell proliferation and invasion were analyzed using hepatocellular carcinoma (HCC) lines, and Western blot (WB) analysis was performed to explore the changes in pathway proteins and ferroptosis proteins. Finally, the drug sensitivity was tested by CCK8 kit. We found that SLC16A3 was significantly upregulated in tumor tissues, and was significantly correlated with TNM stage, histological grade, and macrovascular invasion. TCGA data and WB analysis showed that the high expression of SLC16A3 induced hypoxia, and knockdown could reverse hypoxia and inhibit ERK phosphorylation, thus limiting the malignant behavior of HCC cells. Moreover, knockdown of SLC16A3 significantly increased the level of lipid peroxidation and reactive oxygen species (ROS), while the expressions of GPX4, DHODH and SLC7A11 were inhibited. The expression of SLC16A3 affected the sensitivity of HCC cells to chemotherapy and targeted drugs, and RNA sequencing data suggested that the expression level influenced tumor microenvironment and response to immunotherapy. So, we draw a conclude that SLC16A3 is associated with poor prognosis of HCC. Inhibition of SLC16A3 expression is a potential therapeutic target for HCC.

Research Progress on the Correlation between Acetaldehyde Dehydrogenase 2 and Hepatocellular Carcinoma Development.

Hepatocellular carcinoma (HCC) is the predominant pathologic type of primary liver cancer. It is a malignant tumor of liver epithelial cells. There are many ways to treat HCC, but the survival rate for HCC patients remains low. Therefore, understanding the underlying mechanisms by which HCC occurs and develops is critical to explore new therapeutic targets. Aldehyde dehydrogenase 2 (ALDH2) is an important player in the redox reaction of ethanol with endogenous aldehyde products released by lipid peroxidation. Increasing evidence suggests that ALDH2 is a crucial regulator of human tumor development, including HCC. Therefore, clarifying the relationship between ALDH2 and HCC is helpful for formulating rational treatment strategies. This review highlights the regulatory roles of ALDH2 in the development of HCC, elucidates the multiple potential mechanisms by which ALDH2 regulates the development of HCC, and summarizes the progress of research on ALDH2 gene polymorphisms and HCC susceptibility. Meanwhile, we envision viable strategies for targeting ALDH2 in the treatment of HCC SIGNIFICANCE STATEMENT: Numerous studies have aimed to explore novel therapeutic targets for HCC, and ALDH2 has been reported to be a critical regulator of HCC progression. This review discusses the functions, molecular mechanisms, and clinical significance of ALDH2 in the development of HCC and examines the prospects of ALDH2-based therapy for HCC.

Development of machine learning models for patients in the high intrahepatic cholangiocarcinoma incidence age group.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) has a poor prognosis and is understudied. Based on the clinical features of patients with ICC, we constructed machine learning models to understand their importance on survival and to accurately determine patient prognosis, aiming to develop reference values to guide physicians in developing more effective treatment plans. METHODS: This study used machine learning (ML) algorithms to build prediction models using ICC data on 1,751 patients from the SEER (Surveillance, Epidemiology, and End Results) database and 58 hospital cases. The models' performances were compared using receiver operating characteristic curve analysis, C-index, and Brier scores. RESULTS: A total of eight variables were used to construct the ML models. Our analysis identified the random survival forest model as the best for prognostic prediction. In the training cohort, its C-index, Brier score, and Area Under the Curve values were 0.76, 0.124, and 0.882, respectively, and it also performed well in the test cohort. Kaplan-Meier survival analysis revealed that the model could effectively determine patient prognosis. CONCLUSIONS: To our knowledge, this is the first study to develop ML prognostic models for ICC in the high-incidence age group. Of the ML models, the random survival forest model was best at prognosis prediction.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq data reveals dynamic changes in tumor-associated neutrophils in the tumor microenvironment of hepatocellular carcinoma and leads to the establishment of a neutrophil-related prognostic model.

BACKGROUND: Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment. METHODS: Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored. RESULTS: A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways. CONCLUSION: Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.

Different trends of gastric cancer in China, Japan, Republic of Korea and United States of America.

Gastric cancer exerts a significant healthcare burden worldwide and is highly geographically heterogeneous. This study investigates the burden of gastric cancer in China from 1990 to 2019 and compares it with Japan, South Korea, and the United States. The results indicated a declining trend in ASIR and ASDR in four countries. However, the incidence and death rates in China remain disproportionately high. Significant gender disparities exist in the incidence and death rates, with males experiencing significantly higher rates than females. Incidence and death rates were found to increase with age in all studied countries. In China, a transient upward trend was observed in the period effect, whereas the cohort effect has been declining. In contrast, the remaining countries showed decreasing patterns in both period and cohort effects. The burden of disease remains high in China, therefore, broaden the scope of gastroscopy screening and concentrate on high-risk groups is vital.

Management and outcomes of breast cancer patients with radiotherapy interruption.

Background: Many cancer patients have not received timely treatment or even had treatment interruptions due to the COVID-19 pandemic. The objective of this investigation was to evaluate whether the prognosis of patients with breast cancer after surgery was affected by any interruptions in radiotherapy. Methods: The healthcare documents for breast cancer patients experiencing radiotherapy interruption after surgery, including treatment-related characteristics, and time of interruption, type of disease progression, and survival status, were collected between January and April 2020 during the Wuhan blockade. Results: The final number of patients included was 148, and neither the Kaplan-Meier (KM) survival curve nor the cross-tabulation analysis found statistical significance. Cox regression analysis also did not identify risk factors associated with PFS. Conclusions: The prognosis of patients with postoperative breast cancer may not be significantly impacted by the interruption of radiotherapy, given its integration with additional treatments like targeted and endocrine therapies.

Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.

Background: Most patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients. Methods: Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model's performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique. Results: A total of eight variables were included in the follow-up study, our analysis identified that the gradient boosted machine (GBM) model was the best prognostic model for advanced MHCC. In particular, the GBM model in the training cohort had a C-index of 0.73, a Brier score of 0.124, with area under the curve (AUC) values above 0.78 at the first, third, and fifth year. Importantly, the model also performed well in test cohort. The Kaplan-Meier (K-M) survival analysis demonstrated that the newly developed risk stratification system could well differentiate the prognosis of patients. Conclusion: Of the ML models, GBM model could predict the prognosis of advanced MHCC patients most accurately.

S100A16 is a potential target for reshaping the tumor microenvironment in the hypoxic context of liver cancer.

BACKGROUND: The research on the S100 family has garnered significant attention; however, there remains a dearth of understanding regarding the precise role of S100A16 in the tumor microenvironment of liver cancer. METHOD: Comprehensive analysis was conducted on the expression of S100A16 in tumor tissues and its correlation with hypoxia genes. Furthermore, an investigation was carried out to examine the association between S100A16 and infiltration of immune cells in tumors as well as immunotherapy. Relevant findings were derived from the analysis of single cell sequencing data, focusing on the involvement of S100A16 in both cellular differentiation and intercellular communication. Finally, we validated the expression of S100A16 in liver cancer by Wuhan cohort and multiplexed immunofluorescence to investigate the correlation between S100A16 and hypoxia. RESULT: Tumor tissues displayed a notable increase in the expression of S100A16. A significant correlation was observed between S100A16 and genes associated with hypoxic genes. Examination of immune cell infiltration revealed an inverse association between T cell infiltration and the level of S100A16 expression. The high expression group of S100A16 exhibited a decrease in the expression of genes related to immune cell function. Single-cell sequencing data analysis revealed that non-immune cells predominantly expressed S100A16, and its expression levels increased along with the trajectory of cell differentiation. Additionally, there were significant variations observed in hypoxia genes as cells underwent differentiation. Cellular communication identified non-immune cells interacting with immune cells through multiple signaling pathways. The Wuhan cohort verified that S100A16 expression was increased in liver cancer. The expression of S100A16 and HIF was simultaneously elevated in endothelial cells. CONCLUSION: The strong association between S100A16 and immune cell infiltration is observed in the context of hypoxia, indicating its regulatory role in shaping the hypoxic tumor microenvironment in liver cancer.

The role of interleukin-20 in liver disease: Functions, mechanisms and clinical applications.

Liver disease is a severe public health concern worldwide. There is a close relationship between the liver and cytokines, and liver inflammation from a variety of causes leads to the release and activation of cytokines. The functions of cytokines are complex and variable, and are closely related to their cellular origin, target molecules and mode of action. Interleukin (IL)-20 has been studied as a pro-inflammatory cytokine that is expressed and regulated in some diseases. Furthermore, accumulating evidences has shown that IL-20 is highly expressed in clinical samples from patients with liver disease, promoting the production of pro-inflammatory molecules involved in liver disease progression, and antagonists of IL-20 can effectively inhibit liver injury and produce protective effects. This review highlights the potential of targeting IL-20 in liver diseases, elucidates the potential mechanisms of IL-20 inducing liver injury, and suggests multiple viable strategies to mitigate the pro-inflammatory response to IL-20. Genomic CRISPR/Cas9-based screens may be a feasible way to further explore the signaling pathways and regulation of IL-20 in liver diseases. Nanovector systems targeting IL-20 offer new possibilities for the treatment and prevention of liver diseases.

NUP85 alleviates lipid metabolism and inflammation by regulating PI3K/AKT signaling pathway in nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is one of the common causes of chronic liver disease in the world. The problem of NAFLD had become increasingly prominent. However, its pathogenesis is still indistinct. As we all know, NAFLD begins with the accumulation of triglyceride (TG), leading to fatty degeneration, inflammation and other liver tissues damage. Notably, structure of nucleoporin 85 (NUP85) is related to lipid metabolism and inflammation of liver diseases. In this study, the results of researches indicated that NUP85 played a critical role in NAFLD. Firstly, the expression level of NUP85 in methionine-choline-deficient (MCD)-induced mice increased distinctly, as well as the levels of fat disorder and inflammation. On the contrary, knockdown of NUP85 had the opposite effects. In vitro, AML-12 cells were stimulated with 2 mm free fatty acids (FFA) for 24 h. Results also proved that NUP85 significantly increased in model group, and increased lipid accumulation and inflammation level. Besides, NUP85 protein could interact with C-C motif chemokine receptor 2 (CCR2). Furthermore, when NUP85 protein expressed at an extremely low level, the expression level of CCR2 protein also decreased, accompanied with an inhibition of phosphorylation of phosphoinositol-3 kinase (PI3K)-protein kinase B (AKT) signaling pathway. What is more, trans isomer (ISRIB), a targeted inhibitor of NUP85, could alleviate NAFLD. In summary, our findings suggested that NUP85 functions as an important regulator in NAFLD through modulation of CCR2.

Effect of radiotherapy interruption on nasopharyngeal cancer.

Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial cells of the nasopharynx with a unique geographic distribution, and is particularly prevalent in East and Southeast Asia. Due to its anatomical location, the surgery is difficult to access and the high sensitivity of nasopharyngeal cancer to radiotherapy (RT) makes it the main treatment modality. Radical radiotherapy is the first-line treatment for early-stage nasopharyngeal carcinoma and the cornerstone of multidisciplinary treatment for patients with locally advanced nasopharyngeal carcinoma. Nevertheless, radiotherapy interruption is inevitable as a consequence of unavoidable factors such as public holidays, machine malfunction, patient compliance, and adverse response to treatment, which in turn leads to a reduction in bioactivity and causes sublethal loss of tumor cells to repair. Unirradiated tumor cells are more likely to repopulate at or near their original fastest growth rate during this interval. If no measures are taken after the radiotherapy interruption, such as increasing the dose of radiotherapy and systemic therapy, the tumor is most likely to go uncontrolled and then progress. This review describes the effects of radiotherapy interruption on nasopharyngeal carcinoma, the mechanism of the effect, and explores the measures that can be taken in response to such interruption.

Clinical Features and Prognostic Models in Patients with Intrahepatic Cholangiocarcinoma: a Population-Based Analysis.

BACKGROUND: This study aims to construct a risk classification system and a nomogram in intrahepatic cholangiocarcinomafor patients (ICC). METHODS: Three thousand seven hundred thirty-seven patients diagnosed with ICC between 2010 and 2015 were selected from the Surveillance, Epidemiology and End Results. The consistency index, time-dependent receiver operating characteristic curve, and the calibration plots were adopted to evaluate the effective performance of nomogram. Decision curve analysis (DCA), net reclassification index (NRI), and comprehensive discrimination improvement (IDI) were used to compare the advantages and disadvantages of two models. Kaplan-Meier curve showed the difference in prognosis among different groups. RESULTS: Ten variables were selected to establish the nomogram for ICCA. The C-index (training cohort: 0.765, P < 0.05; validation cohort: 0.776, P < 0.05) and the time-dependent AUCs (the training cohort: the values of 1, 3, 5 years were 0.836, 0.873, and 0.888; the validation cohort: the values of 1, 3, 5 years were 0.833, 0.838, and 0.881) showed satisfactory discrimination. The calibration curves also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 3-, 5-year CSS: 0.879, 0.94, 0.771; validation cohort: 1-, 3-, 5-year CSS: 0.905, 0.945, 0.717) and IDI (training cohort: 1-, 3-, 5-year CSS: 0.24, 0.23, 0.22; validation cohort: 1-, 3-, 5-year CSS: 0.24, 0.46, 0.27) (P < 0.05) (compared with AJCC staging). DCA showed that the new model was more practical and had better recognition than AJCC staging. CONCLUSIONS: A new risk stratification system for ICC patients has been developed, which can be a practical tool for patient management.

New treatment insights into pancreatic acinar cell carcinoma: case report and literature review.

Pancreatic acinar cell carcinoma (PACC) is a rare pancreatic malignancy with unique clinical, molecular, and morphologic features. The long-term survival of patients with PACC is substantially better than that of patients with ductal adenocarcinoma of the pancreas. Surgical resection is considered the first choice for treatment; however, there is no standard treatment option for patients with inoperable disease. The patient with metastatic PACC reported herein survived for more than 5 years with various treatments including chemotherapy, radiotherapy, antiangiogenic therapy and combined immunotherapy.

Tumor-associated endothelial cell prognostic risk model and tumor immune environment modulation in liver cancer based on single-cell and bulk RNA sequencing: Experimental verification.

BACKGROUND: To build a prognostic and immunotherapeutic response prediction model for liver cancer based on marker genes of tumor-associated endothelial cell (TEC). METHOD: Single cell sequencing data from Gene Expression Omnibus (GEO) liver cancer patients were utilized to identify TEC subpopulations. Models were built from transcriptomic and clinical data of TCGA liver cancer patients. The GSE76427 and ICGC databases were used as independent validation sets. Time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to verify the ability of the model to predict survival. XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA were applied to evaluate tumor immune cell infiltration. The TIDE score was used to predict the effect of immunotherapy. Immune blockade checkpoint gene, tumor mutational load and GSVA enrichment analyses were further explored. The expression levels of candidate genes were measured and validated by real-time PCR between liver cancer tissues and adjacent nontumor liver tissues. RESULTS: Eighty-seven genes were identified as marker genes for TECs. IGFBP3, RHOC, S100A16, FSCN1, and CLEC3B were included in the constructed prognostic model. Time-dependent ROC curve values were higher than 0.700 in both the model and validation groups. The low risk group exhibited high immune cell infiltration and function than the higher risk group. The TIDE score indicated that the low-risk group benefited more from immunotherapy than the high-risk group. The risk score and multiple immune blockade checkpoint genes and immune-related pathways were strongly correlated. CONCLUSION: Novel signatures of TEC marker genes showed a powerful ability to predict prognosis and immunotherapy response in patients with liver cancer.

Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy.

Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted. Results: The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups. Conclusion: WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Prognostic modeling of NK cell marker genes in patients with hepatocellular carcinoma based on single cell sequencing and transcriptome data analysis. METHODS: Marker genes of NK cells were analyzed according to single cell sequencing data of hepatocellular carcinoma. Univariate Cox regression, lasso regression analysis, and multivariate Cox regression were performed to estimate the prognostic value of NK cell marker genes. TCGA, GEO and ICGC transcriptomic data were applied to build and validate the model. Patients were divided into high and low risk groups based on the median risk score. XCELL, timer, quantitative sequences, MCP counter, EPIC, CIBERSORT and CIBERSORT-abs were performed to explore the relationship between risk score and tumor microenvironment in hepatocellular carcinoma. Finally the sensitivity of the model to chemotherapeutic agents was predicted. RESULTS: Single-cell sequencing identified 207 marker genes for NK cells in hepatocellular carcinoma. Enrichment analysis suggested that NK cell marker genes were mainly involved in cellular immune function. Eight genes were selected for prognostic modeling after multifactorial COX regression analysis. The model was validated in GEO and ICGC data. Immune cell infiltration and function were higher in the low-risk group than in the high-risk group. The low-risk group was more suitable for ICI and PD-1 therapy. Half-maximal inhibitory concentrations of Sorafenib, Lapatinib, Dabrafenib, and Axitinib were significantly different on the two risk groups. CONCLUSION: A new signature of hepatocyte NK cell marker genes possesses a powerful ability to predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma.

Clinical features and prognostic factors in patients diagnosed with lymphovascular invasion of testicular germ-cell tumors: Analysis based on the SEER database.

Background: Lymphovascular invasion (LVI) is a high-risk factor for testicular germ-cell tumors (TGCT), but a prognostic model for TGCT-LVI patients is lacking. This study aimed to develop a nomogram for predicting the overall survival (OS) of TGCT-LVI patients. Methods: A complete cohort of 3288 eligible TGCG-LVI patients (training cohort, 2300 cases; validation cohort, 988 cases) were obtained from the Surveillance, Epidemiology, and End Results database. Variables screened by multivariate Cox regression analysis were used to construct a nomogram, which was subsequently evaluated using the consistency index (C-index), time-dependent receiver operating characteristic curve (ROC), and calibration plots. The advantages and disadvantages of the American Joint Committee on Cancer (AJCC) staging system and the nomogram were assessed by integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Decision-analysis curve (DCA) was used to measure the net clinical benefit of the nomogram versus the AJCC staging system. Finally, Kaplan-Meier curves were used to evaluate the ability to identify different risk groups between the traditional AJCC staging system and the new risk-stratification system built on the nomogram. Results: Nine variables were screened by multivariate Cox regression analysis to construct the nomogram. The C-index (training cohort, 0.821; validation cohort, 0.819) and time-dependent ROC of 3-, 5-, and 9-year OS between the two cohorts suggested that the nomogram had good discriminatory ability. Calibration curves showed good consistency of the nomogram. The NRI values of 3-, 5-, and 9-year OS were 0.308, 0.274, and 0.295, respectively, and the corresponding values for the validation cohort were 0.093, 0.093, and 0.099, respectively (P<0.01). Additionally, the nomogram had more net clinical benefit as shown by the DCA curves, and the new risk-stratification system provided better differentiation than the AJCC staging system. Conclusions: A prognostic nomogram and new risk-stratification system were developed and validated to assist clinicians in assessing TGCT-LVI patients.

Recent advances of Phosphodiesterase 4B in cancer.

INTRODUCTION: Phosphodiesterase 4B (PDE4B) is a crucial enzyme in the phosphodiesterases (PDEs), acting as a regulator of cyclic adenosine monophosphate (cAMP). It is involved in cancer process through PDE4B/cAMP signaling pathway. Cancer occurs and develops with the regulation of PDE4B in the body, suggesting that PDE4B is a promising therapeutic target. AREAS COVERED: This review covereed the function and mechanism of PDE4B in cancer. We summarized the possible clinical applications of PDE4B, and highlighted the possible ways to develop clinical applications of PDE4B inhibitors. We also discussed some common PDEs inhibitors, and expected the development of combined targeting PDE4B and other PDEs drugs in the future. EXPERT OPINION: The existing research and clinical data can strongly prove the role of PDE4B in cancer. PDE4B inhibition can effectively increase cell apoptosis, inhibit cell proliferation, transformation, migration, etc., indicating that PDE4B inhibition can effectively inhibit the development of cancer. Other PDEs may antagonize or coordinate this effect. As for the further study on the relationship between PDE4B and other PDEs in cancer, it is still a challenge to develop multi-targeted PDEs inhibitors.

The role of CSTF2 in cancer: from technology to clinical application.

A protein called cleavage-stimulating factor subunit 2 (CSTF2, additionally called CSTF-64) binds RNA and is needed for the cleavage and polyadenylation of mRNA. CSTF2 is an important component subunit of the cleavage stimulating factor (CSTF), which is located on the X chromosome and encodes 557 amino acids. There is compelling evidence linking elevated CSTF2 expression to the pathological advancement of cancer and on its impact on the clinical aspects of the disease. The progression of cancers, including hepatocellular carcinoma, melanoma, prostate cancer, breast cancer, and pancreatic cancer, is correlated with the upregulation of CSTF2 expression. This review provides a fresh perspective on the investigation of the associations between CSTF2 and various malignancies and highlights current studies on the regulation of CSTF2. In particular, the mechanism of action and potential clinical applications of CSTF2 in cancer suggest that CSTF2 can serve as a new biomarker and individualized treatment target for a variety of cancer types.

The role of acetaldehyde dehydrogenase 2 in the pathogenesis of liver diseases.

Common liver tissue damage is mainly due to the accumulation of toxic aldehydes in lipid peroxidation under oxidative stress. Cumulative toxic aldehydes in the liver can be effectively metabolized by acetaldehyde dehydrogenase 2 (ALDH2), thereby alleviating various liver diseases. Notably, gene mutation of ALDH2 leads to impaired ALDH2 enzyme activity, thus aggravating the progress of liver diseases. However, the relationship and specific mechanism between ALDH2 and liver diseases are not clear. Consequently, the review explains the relationship between ALDH2 and liver diseases such as alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), liver fibrosis and hepatocellular carcinoma (HCC). In addition, this review also discusses ALDH2 as a potential therapeutic target for various liver diseases,and focuses on summarizing the regulatory mechanism of ALDH2 in these liver diseases.