Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis.

BACKGROUND: Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets. METHODS: Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms. RESULTS: The Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo-keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity. CONCLUSION: These three genes are pivotal mitochondrial genes implicated in NASH progression.

Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer.

Background: T cells are crucial components of antitumor immunity. A list of genes associated with T cell proliferation was recently identified; however, the impact of T cell proliferation-related genes (TRGs) on the prognosis and therapeutic responses of patients with colorectal cancer (CRC) remains unclear. Methods: 33 TRG expression information and clinical information of patients with CRC gathered from multiple datasets were subjected to bioinformatic analysis. Consensus clustering was used to determine the molecular subtypes associated with T cell proliferation. Utilizing the Lasso-Cox regression, a predictive signature was created and verified in external cohorts. A tumor immune environment analysis was conducted, and potential biomarkers and therapeutic drugs were identified and confirmed via in vitro and in vivo studies. Results: CRC patients were separated into two TRG clusters, and differentially expressed genes (DEGs) were identified. Patient information was divided into three different gene clusters, and the determined molecular subtypes were linked to patient survival, immune cells, and immune functions. Prognosis-associated DEGs in the three gene clusters were used to evaluate the risk score, and a predictive signature was developed. The ability of the risk score to predict patient survival and treatment response has been successfully validated using multiple datasets. To discover more possible biomarkers for CRC, the weighted gene co-expression network analysis algorithm was utilized to screen key TRG variations between groups with high- and low-risk. CDK1, BATF, IL1RN, and ITM2A were screened out as key TRGs, and the expression of key TRGs was confirmed using real-time reverse transcription polymerase chain reaction. According to the key TRGs, 7,8-benzoflavone was identified as the most significant drug molecule, and MTT, colony formation, wound healing, transwell assays, and in vivo experiments indicated that 7,8-benzoflavone significantly suppressed the proliferation and migration of CRC cells. Conclusion: T cell proliferation-based molecular subtypes and predictive signatures can be utilized to anticipate patient results, immunological landscape, and treatment response in CRC. Novel biomarker candidates and potential therapeutic drugs for CRC were identified and verified using in vitro and in vivo tests.

Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer.

BACKGROUND: A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). METHODS: 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified. RESULTS: The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. CONCLUSION: We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.

Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis.

BACKGROUND: Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis. METHODS: To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used. RESULTS: The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures. CONCLUSION: ZFP36 is a key FRG involved in NASH pathogenesis.

Risk factors analysis and survival prediction model establishment of patients with lung adenocarcinoma based on different pyroptosis-related gene subtypes.

BACKGROUND: Lung adenocarcinoma (LUAD) is a common cancer with a poor prognosis. Pyroptosis is an important process in the development and progression of LUAD. We analyzed the risk factors affecting the prognosis of patients and constructed a nomogram to predict the overall survival of patients based on different pyroptosis-related genes (PRGs) subtypes. METHODS: The genomic data of LUAD were downloaded from the TCGA and GEO databases, and all data were filtered and divided into TCGA and GEO cohorts. The process of data analysis and visualization was performed via R software. The data were classified based on different PRGs subtypes using the K-means clustering method. Then, the differentially expressed genes were identified between two different subtypes, and risk factors analysis, survival analysis, functional enrichment analysis, and immune cells infiltration landscape analysis were conducted. The COX regression analysis was used to construct the prediction model. RESULTS: Based on the PRGs of LUAD, the patients were divided into two subtypes. We found the survival probability of patients in subtype 1 is higher than that in subtype 2. The results of the logistics analysis showed that gene risk score was closely associated with the prognosis of LUAD patients. The results of GO analysis and KEGG analysis revealed important biological processes and signaling pathways involved in the differentially expressed proteins between the two subtypes. Then we constructed a prediction model of patients' prognosis based on 13 genes, including IL-1A, P2RX1, GSTM2, ESYT3, ZNF682, KCNF1, STK32A, HHIPL2, GDF10, NDC80, GSTA1, BCL2L10, and CCR2. This model was strongly related to the overall survival (OS) and also reflects the immune status in patients with LUAD. CONCLUSION: In our study, we examined LUAD heterogeneity with reference to pyroptosis and found different prognoses between the two subtypes. And a novel prediction model was constructed to predict the OS of LUAD patients based on different PRGs signatures. The model has shown excellent predictive efficiency through validation.

Prognosis signature for predicting the survival and immunotherapy response in esophageal carcinoma based on cellular senescence-related genes.

Background: Cellular senescence occurs throughout life and can play beneficial roles in a variety of physiological processes, including embryonic development, tissue repair, and tumor suppression. However, the relationship between cellular senescence-related genes (CSRGs) and immunotherapy in esophageal carcinoma (ECa) remains poorly defined. Methods: The data set used in the analysis was retrieved from TCGA (Research Resource Identifier (RRID): SCR_003193), GEO (RRID: SCR_005012), and CellAge databases. Data processing, statistical analysis, and diagram formation were conducted in R software (RRID: SCR_001905) and GraphPad Prism (RRID: SCR_002798). Based on CSRGs, we used the TCGA database to construct a prognostic signature for ECa and then validated it in the GEO database. The predictive efficiency of the signature was evaluated using receiver operating characteristic (ROC) curves, Cox regression analysis, nomogram, and calibration curves. According to the median risk score derived from CSRGs, patients with ECa were divided into high- and low-risk groups. Immune infiltration and immunotherapy were also analyzed between the two risk groups. Finally, the hub genes of the differences between the two risk groups were identified by the STRING (RRID: SCR_005223) database and Cytoscape (RRID: SCR_003032) software. Results: A six-gene risk signature (DEK, RUNX1, SMARCA4, SREBF1, TERT, and TOP1) was constructed in the TCGA database. Patients in the high-risk group had a worse overall survival (OS) was disclosed by survival analysis. As expected, the signature presented equally prognostic significance in the GSE53624 cohort. Next, the Area Under ROC Curve (AUC=0.854) and multivariate Cox regression analysis (HR=3.381, 2.073-5.514, P<0.001) also proved that the risk signature has a high predictive ability. Furthermore, we can more accurately predict the prognosis of patients with ECa by nomogram constructed by risk score. The result of the TIDE algorithm showed that ECa patients in the high-risk group had a greater possibility of immune escape. At last, a total of ten hub genes (APOA1, MUC5AC, GC, APOA4, AMBP, FABP1, APOA2, SOX2, MUC8, MUC17) between two risk groups with the highest interaction degrees were identified. By further analysis, four hub genes (APOA4, AMBP, FABP1, and APOA2) were related to the survival differences of ECa. Conclusions: Our study reveals comprehensive clues that a novel signature based on CSRGs may provide reliable prognosis prediction and insight into new therapy for patients with ECa.

Fabrication of Black Silicon Microneedle Arrays for High Drug Loading.

Silicon microneedle (Si-MN) systems are a promising strategy for transdermal drug delivery due to their minimal invasiveness and ease of processing and application. Traditional Si-MN arrays are usually fabricated by using micro-electro-mechanical system (MEMS) processes, which are expensive and not suitable for large-scale manufacturing and applications. In addition, Si-MNs have a smooth surface, making it difficult for them to achieve high-dose drug delivery. Herein, we demonstrate a solid strategy to prepare a novel black silicon microneedle (BSi-MN) patch with ultra-hydrophilic surfaces for high drug loading. The proposed strategy consists of a simple fabrication of plain Si-MNs and a subsequent fabrication of black silicon nanowires. First, plain Si-MNs were prepared via a simple method consisting of laser patterning and alkaline etching. The nanowire structures were then prepared on the surfaces of the plain Si-MNs to form the BSi-MNs through Ag-catalyzed chemical etching. The effects of preparation parameters, including Ag+ and HF concentrations during Ag nanoparticle deposition and [HF/(HF + H2O2)] ratio during Ag-catalyzed chemical etching, on the morphology and properties of the BSi-MNs were investigated in detail. The results show that the final prepared BSi-MN patches exhibit an excellent drug loading capability, more than twice that of plain Si-MN patches with the same area, while maintaining comparable mechanical properties for practical skin piercing applications. Moreover, the BSi-MNs exhibit a certain antimicrobial activity that is expected to prevent bacterial growth and disinfect the affected area when applied to the skin.

Electroreductive Dicarboxylation of Unactivated Skipped Dienes with CO2.

Carboxylation of easily available alkenes with CO2 is highly important to afford value-added carboxylic acids. Although dicarboxylation of activated alkenes, especially 1,3-dienes, has been widely investigated, the challenging dicarboxylation of unactivated 1,n-dienes (n>3) with CO2 remains unexplored. Herein, we report the first dicarboxylation of unactivated skipped dienes with CO2 via electrochemistry, affording valuable dicarboxylic acids. Control experiments and DFT calculations support the single electron transfer (SET) reduction of CO2 to its radical anion, which is followed by sluggish radical addition to unactivated alkenes, SET reduction of unstabilized alkyl radicals to carbanions and nucleophilic attack on CO2 to give desired products. This reaction features mild reaction conditions, broad substrate scope, facile derivations of products and promising application in polymer chemistry.

A novel oxidative stress- and ferroptosis-related gene prognostic signature for distinguishing cold and hot tumors in colorectal cancer.

Oxidative stress and ferroptosis exhibit crosstalk in many types of human diseases, including malignant tumors. We aimed to develop an oxidative stress- and ferroptosis-related gene (OFRG) prognostic signature to predict the prognosis and therapeutic response in patients with colorectal cancer (CRC). Thirty-four insertion genes between oxidative stress-related genes and ferroptosis-related genes were identified as OFRGs. We then performed bioinformatics analysis of the expression profiles of 34 OFRGs and clinical information of patients obtained from multiple datasets. Patients with CRC were divided into three OFRG clusters, and differentially expressed genes (DEGs) between clusters were identified. OFRG clusters correlated with patient survival and immune cell infiltration. Prognosis-related DEGs in three clusters were used to calculate the risk score, and a prognostic signature was constructed according to the risk score. In this study, patients in the low-risk group had better prognosis, higher immune cell infiltration levels, and better responses to fluorouracil-based chemotherapy and immune checkpoint blockade therapy than high-risk patients; these results were successfully validated with multiple independent datasets. Thus, low-risk CRC could be defined as hot tumors and high-risk CRC could be defined as cold tumors. To further identify potential biomarkers for CRC, the expression levels of five signature genes in CRC and adjacent normal tissues were further verified via an in vitro experiment. In conclusion, we identified 34 OFRGs and constructed an OFRG-related prognostic signature, which showed excellent performance in predicting survival and therapeutic responses for patients with CRC. This could help to distinguish cold and hot tumors in CRC, and the results might be helpful for precise treatment protocols in clinical practice.

PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer.

PANoptosis is a newly-discovered cell death pathway that involves crosstalk and co-ordination between pyroptosis, apoptosis, and necroptosis processes. However, the roles of PANoptosis-related genes (PRGs) in prognosis and immune landscape of colon cancer remain widely unknown. Here, we performed a bioinformatics analysis of expression data of nineteen PRGs identified from previous studies and clinical data of colon cancer patients obtained from TCGA and GEO databases. Colon cancer cases were divided into two PRG clusters, and prognosis-related differentially expressed genes (PRDEGs) were identified. The patient data were then separated into two corresponding distinct gene clusters, and the relationship between the risk score, patient prognosis, and immune landscape was analyzed. The identified PRGs and gene clusters correlated with patient survival and immune system and cancer-related biological processes and pathways. A prognosis signature based on seven genes was identified, and patients were divided into high-risk and low-risk groups based on the calculated risk score. A nomogram model for prediction of patient survival was also developed based on the risk score and other clinical features. Accordingly, the high-risk group showed worse prognosis, and the risk score was related to immune cell abundance, cancer stem cell (CSC) index, checkpoint expression, and response to immunotherapy and chemotherapeutic drugs. Results of quantitative real-time polymerase chain reaction (qRT-PCR) showed that LGR5 and VSIG4 were differentially expressed between normal and colon cancer samples. In conclusion, we demonstrated the potential of PANoptosis-based molecular clustering and prognostic signatures for prediction of patient survival and tumor microenvironment (TME) in colon cancer. Our findings may improve our understanding of the role of PANoptosis in colon cancer, and enable the development of more effective treatment strategies.

Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer.

The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was developed by performing the LASSO regression analysis. Finally, PCA analysis, t-SNE analysis, Kaplan-Meier survival analysis, ROC analysis, univariate Cox regression analysis, multivariate Cox regression analysis, C-index analysis, meta-analysis, immune infiltration analysis, and IPS score analysis were used to evaluate the significance of the risk signature for predicting prognosis and immunotherapy response in colon cancer. The colon cancer data were classified into three clusters. The patients in cluster A and cluster B had longer survival. A cellular senescence-related risk score signature was developed. Patients in the low-risk score group showed a better prognosis. The risk score signature could predict colon cancer patients' prognosis independently of other clinical characteristics. The risk score signature predicted the prognosis of colon cancer patients more accurately than other signatures. Patients in the low-risk score group showed a better response to immunotherapy. The opposite was true for the high-risk score group. In conclusion, the cellular senescence-related risk score signature could be used for the prediction of prognosis and immunotherapy response in colon cancer.

Risk Factors Analysis and Prediction Model Establishment of Intestinal Metaplasia or Dysplasia in Patients With Chronic Atrophic Gastritis: A Multi-Center Retrospective Study.

Objective: To investigate the risk factors and construct a prediction model of chronic atrophic gastritis (CAG) patients with intestinal metaplasia or dysplasia. Method: The clinical data of 450 patients with CAG who were diagnosed and treated in the Department of Gastroenterology of the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine from June 2016 to February 2022 were collected. Single and multiple factors logistic regression analysis were used to explore the risk factors of intestinal metaplasia or dysplasia in patients of training cohort. Then, we constructed a model to predict the onset of intestinal metaplasia or dysplasia based on the data of training cohort, following which we tested the model in an external validation cohort of 193 patients from a local university teaching hospital. The ROC curve, calibration curve, and decision curve analysis were used to evaluate the accuracy of the prediction model. Result: Helicobacter pylori (H. pylori, HP) infection, pepsinogen I, gastrin-17, and the number of lesions were found to be independent rick factors of the model. The liner prediction model showed excellent predictive value in both training cohort and validation cohort. Conclusion: HP infection, pepsinogen I, gastrin-17, and the number of lesions are independent risk factors for intestinal metaplasia or dysplasia in patients with CAG. The prediction model constructed based on these factors has a high accuracy and excellent calibration, which can provide a great basis for condition assessment and individualized treatment of the patients.

Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer.

Our study aimed to explore the effect of cellular senescence and to find potential therapeutic strategies for gastric cancer. Cellular senescence-related genes were acquired from the CellAge database, while gastric cancer data were obtained from GEO and TCGA databases. SMARCA4 had the highest mutation frequency (6%), and it was linked to higher overall survival (OS) and progression-free survival (PFS). The gastric cancer data in TCGA database served as a training set to construct a prognostic risk score signature, and GEO data were used as a testing set to validate the accuracy of the signature. Patients with the low-risk score group had a longer survival time, while the high-risk score group is the opposite. Patients with low-risk scores had higher immune infiltration and active immune-related pathways. The results of drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Most patients with mutation genes had a lower risk score than the wild type. Therefore, the risk score signature with cellular senescence-related genes can predict gastric cancer prognosis and identify gastric cancer patients who are sensitive to chemotherapy and immunotherapy.

Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer.

Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs). Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer. Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues. Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics.

LncRNA LINC01134 Contributes to Radioresistance in Hepatocellular Carcinoma by Regulating DNA Damage Response via MAPK Signaling Pathway.

Hepatocellular carcinoma (HCC) is a highly mortal cancer that could be treated by radiotherapy. DNA damage response (DDR) is a vital factor affecting cancer development after radiotherapy. Long non-coding RNAs (lncRNAs) have been revealed to regulate DNA damage response and repair in cancer cells. Nevertheless, the function of long intergenic non-protein coding RNA 1134 (LINC01134) has not been explored in DDR. In this study, we targeted digging into the function of LINC01134 in DDR and exploring the underlying mechanism in HCC cells. RT-qPCR was employed to measure LINC01134 expression, and we found LINC01134 was significantly upregulated in HCC cells. Functional analysis suggested that LINC01134 depletion attenuated radioresistance of HCC cells by facilitating DNA damage. In vivo assays demonstrated LINC01134 depletion hindered HCC tumor growth. Mechanism assays unveiled LINC01134 sequestered microRNA-342-3p (miR-342-3p) and recruited insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) protein to modulate mitogen-activated protein kinase 1 (MAPK1) expression, consequently activating MAPK signaling pathway. Rescue assays validated the LINC01134/miR-342-3p/MAPK1 axis in the radio-resistant HCC cells. In conclusion, LINC01134 might be identified to be a useful biomarker for the therapy of HCC.

WTAP facilitates progression of hepatocellular carcinoma via m6A-HuR-dependent epigenetic silencing of ETS1.

BACKGROUND: N6-methyladenosine (m6A) methylation, a well-known modification with new epigenetic functions, has been reported to participate in the tumorigenesis of hepatocellular carcinoma (HCC), providing novel insights into the molecular pathogenesis of this disease. However, as the key component of m6A methylation, Wilms tumor 1-associated protein (WTAP) has not been well studied in HCC. Here we investigated the biological role and underlying mechanism of WTAP in liver cancer. METHODS: We determined the expression of WTAP and its correlation with clinicopathological features using tissue microarrays and the Cancer Genome Atlas (TCGA) dataset. And we clarified the effects of WTAP on HCC cells using cell proliferation assay, colony formation, Edu assay and subcutaneous xenograft experiments. We then applied RNA sequencing combined with gene expression omnibus (GEO) data to screen candidate targets of WTAP. Finally, we investigated the regulatory mechanism of WTAP in HCC by m6A dot blot assay, methylated RNA immunoprecipitation (MeRIP) assay, dual luciferase reporter assay, RNA immunoprecipitation (RIP) assay and Chromatin immunoprecipitation (ChIP) assay. RESULTS: We demonstrated that WTAP was highly expressed in HCC which indicated the poor prognosis, and that WTAP expression served as an independent predictor of HCC survival. Functionally, WTAP promoted the proliferation capability and tumor growth of HCC cells in vitro and in vivo. Furthermore, ETS proto-oncogene 1 (ETS1) was identified as the downstream effector of WTAP. The m6A modification regulated by WTAP led to post-transcriptional suppression of ETS1, with the implication of Hu-Antigen R (HuR) as an RNA stabilizer. Then ETS1 was found to inhibit the progression of HCC and could rescue the phenotype induced by WTAP deficiency. Moreover, WTAP modulated the G2/M phase of HCC cells through a p21/p27-dependent pattern mediated by ETS1. CONCLUSION: We have identified that WTAP is significantly up-regulated in HCC and promotes liver cancer development. WTAP-guided m6A modification contributes to the progression of HCC via the HuR-ETS1-p21/p27 axis. Our study is the first to report that WTAP-mediated m6A methylation has a crucial role in HCC oncogenesis, and highlights WTAP as a potential therapeutic target of HCC treatment.

Upregulated expression of HOXB7 in intrahepatic cholangiocarcinoma is associated with tumor cell metastasis and poor prognosis.

Homeobox B7 (HOXB7) protein is reported to be aberrantly expressed in a variety of cancers and to play an important role in multiple cellular processes. However, the specific mechanism by which HOXB7 promotes the malignant progression of intrahepatic cholangiocarcinoma (ICC) remains unclear. Therefore, we used quantitative real-time polymerase chain reaction (PCR) to detect the expression level of HOXB7 in 38 paired ICC tissue samples. Additionally, to assess correlation between HOXB7 and ICC prognosis, we performed immunohistochemistry (IHC) using 122 ICC tissues to detect HOXB7 expression. Cell Counting Kit-8 (CCK-8) and colony formation assays were employed to assess ICC cell proliferation, and Transwell assays were performed to estimate the invasion and migration abilities of ICC cells. The capillary tube formation assay was applied to explore the angiogenic effects of HOXB7. A xenograft tumor model was established in nude mice to assess the role of HOXB7 in tumor growth and lung metastasis. The results showed higher expression of HOXB7 in ICC tissues than in noncancerous tissues, and this increased expression was significantly associated with a poor prognosis. In addition, HOXB7 overexpression enhanced capillary tube formation, invasion and migration of ICC cells in vitro, whereas HOXB7 knockdown produced the opposite results in vitro. Moreover, the role of HOXB7 in promoting tumor growth and metastasis was verified in vivo. Further investigation revealed that the expression levels of MMP2, MMP9, VEGFa, and IL8 were elevated by HOXB7 and that the ERK pathway was activated. Our results demonstrate the prognostic value of HOXB7 and its role in metastasis and angiogenesis in ICC. HOXB7 upregulated MMP2, MMP9, VEGFa, and IL8 expression via the ERK pathway to accelerate the malignant progression of ICC.

The HDAC Inhibitor Quisinostat (JNJ-26481585) Supresses Hepatocellular Carcinoma alone and Synergistically in Combination with Sorafenib by G0/G1 phase arrest and Apoptosis induction.

The high activity of Histone deacetylases (HDACs) in hepatocellular carcinoma (HCC) usually positively correlates with poor prognosis of patients. Accordingly histone deacetylases inhibitors (HDACis) are considered to be potential agents treating patients with HCC. In our study, we evaluated effect of quisinostat alone and in combination with sorafenib in HCC cells via inducing G0/G1 phase arrest through PI3K/AKT/p21 pathway and apoptosis by JNK/c-Jun/caspase3 pathway in vitro and in vivo. The proliferation assay and flow cytometry were used to measure the viability, cell cycle and apoptosis. And Western blot assay was carried out to determine expression alternations of related proteins. Moreover HCCLM3 xenograft was further performed to detect antitumor effect of quisinostat in vivo. Here, we found that quisinostat impeded cell proliferation, and remarkably induced G0/G1 phase arrest and apoptosis in HCC cells in a dose-dependent manner. G0/G1 phase arrest was observed by alterations in PI3K/AKT/p21 proteins. Meanwhile the JNK, c-jun and caspase-3 were activated by quisinostat in a dose-dependent manner. Correspondingly quisinostat facilitated G0/G1 cycle arrest and apoptosis in HCC cells through PI3K/AKT/p21 pathways and JNK/c- jun/caspase3 pathways. Moreover, the potent tumor-suppressive effects facilitated by quisinostat, was significantly potentiated by combination with sorafenib in vitro and vivo. The combination treatment of quisinostat and sorafenib markedly suppressed cell proliferation and induced apoptosis in a synergistic manner. Moreover the therapy of quisinostat combined with sorafenib could apparently decrease tumor volume of a HCCLM3 xenograft model. Our study indicated that quisinostat, as a novel chemotherapy for HCC, exhibited excellent antitumor activity in vitro and vivo, which was even enhanced by the addition of sorafenib, implying combination of quisinostat with sorafenib a promising and alternative therapy for patients with advanced hepatocellular carcinoma.

14-3-3σ downregulation suppresses ICC metastasis via impairing migration, invasion, and anoikis resistance of ICC cells.

BACKGROUND: 14-3-3σ protein plays an important role in multiple cellular processes. The role of 14-3-3σ in the progression of intrahepatic cholangiocarcinoma (ICC) has not been well understood. OBJECTIVE: We performed this research to explore the relationship between 14-3-3σ level and clinical characteristics and prognosis of ICC patients. Besides, we used ICC cell lines HCCC-9810 and RBE to assess the biological function of 14-3-3σ. METHODS: We examined 14-3-3σ expression in 28 ICC tissues and matched paratumor tissues by quantitative real-time PCR and immunohistochemistry. Additionally, ICC tissue array from 100 patients and normal liver tissue array from 24 healthy people were also analyzed by immunohistochemistry. 14-3-3σ was knocked down in ICC cell lines and the functions and mechanisms of 14-3-3σ were assessed. RESULTS: 14-3-3σ is highly expressed in ICC tissues and high expression of 14-3-3σ correlates poor overall survival in ICC patients. Knocking down of 14-3-3σ in ICC cell lines reduced cells migration, invasion and anoikis resistance. Furthermore, 14-3-3σ-silenced ICC cells showed significantly decreased invasion-related protein MMP2 and MMP9 expression. CONCLUSIONS: Our results demonstrate prognostic value of 14-3-3σ and its role in metastasis, which is associated with ICC cell lines migration, invasion and anoikis resistance.