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Hepatocellular carcinoma (HCC) emerges as the third leading cause of cancer mortality, contributing to approximately 830,000 deaths annually. The mechanisms driving its pathogenesis remain largely elusive. Through bioinformatic scrutiny, Mitochondrial Carrier 1 (MTCH1), a component of the mitochondrial carrier family, has been pinpointed as potentially pivotal in HCC evolution. Examination of The Cancer Genome Atlas (TCGA) database indicated a pronounced increase in MTCH1 expression within HCC tissues versus normal liver counterparts. Subsequent analyses, utilizing both Kaplan-Meier mapper and Gene Expression Profiling Interactive Analysis (GEPIA) datasets, associated elevated MTCH1 levels with reduced overall survival (OS) and disease-free survival (DFS). Complementary in vitro assessments confirmed that MTCH1 downregulation suppresses HCC cell proliferation and notably diminishes HCC xenograft tumor growth in murine models. Additional explorations, including Gene Set Enrichment Analysis (GSEA), STRING database interrogation, and quantitative PCR (qPCR) experiments, suggest MTCH1's involvement in HCC progression via the CDK-RB-E2F signaling axis. Collectively, these insights endorse MTCH1 as a promising therapeutic target for HCC, underscoring its significance in the disease's molecular framework and potential treatment innovation.
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Biomarcadores de Tumor , Carcinoma Hepatocelular , Neoplasias Hepáticas , Animales , Humanos , Ratones , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patología , Carcinoma Hepatocelular/metabolismo , Proliferación Celular/genética , Regulación Neoplásica de la Expresión Génica , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/metabolismo , PronósticoRESUMEN
ZW10 interacting kinetochore protein (ZWINT), an essential part of the kinetochore complex, plays a crucial role in maintaining genome stability by correcting improper attachments between the kinetochore and microtubules. An initial analysis of The Cancer Genome Atlas and Gene Expression Omnibus databases revealed that ZWINT is significantly expressed across a diverse range of tumor types. We subsequently investigated the influence of ZWINT on clinical outcomes and potential signaling pathways. A multidimensional analysis of ZWINT revealed significant statistical associations between ZWINT expression and clinical outcomes, as well as the E2F1 oncogenic signature. Experimental validation confirmed the increased expression of ZWINT in both pancreatic cancer cell lines and pancreatic adenocarcinoma tissues. Furthermore, our findings indicate that ZWINT promotes the proliferation of PANC-1 cells through cell cycle regulation. This comprehensive analysis of ZWINT suggests a strong correlation between its expression and various types of tumors, especially pancreatic adenocarcinoma (PAAD), indicating its potential oncogenic role. These findings enhance our understanding of the function of ZWINT in carcinogenesis.
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Accumulating evidence has indicated an increased risk of acute pancreatitis in individuals with inflammatory bowel disease (IBD); however, the establishment of a clear and direct causal connection between IBD and acute pancreatitis remains uncertain. Utilizing genetic data from publicly accessible genome-wide association studies (GWAS), we conducted a 2-sample MR analysis to identify the associations between IBD, ulcerative colitis (UC), Crohn disease (CD), and acute pancreatitis risk. Rigorous quality control steps ensured the selection of eligible single nucleotide polymorphisms (SNPs) with strong associations to IBD. The primary estimation used the inverse-variance weighted method. We also assessed heterogeneity, potential pleiotropy, and conducted sensitivity analyses. The direction of causality was confirmed using the Steiger test. The MR analysis showed that IBD increased the risk of acute pancreatitis (IVW: OR = 1.032, 95% CI: 1.006-1.06, P = .015). Among the subgroup of IBD, CD (IVW: OR = 1.034, 95% CI: 1.008-1.06, P = .007) indicates a significant increase in the risk of acute pancreatitis compared to UC (IVW: OR = 1.02, 95% CI: 0.99-1.051, P = .189). The MR analysis assessing the association between CD and acute pancreatitis showed no evidence of heterogeneity or horizontal pleiotropy. Likewise, the leave-one-out (LOO) method indicated no significant influence of any individual SNP on the overall findings. In addition, the Steiger direction test revealed that CD was the cause for increased risk of acute pancreatitis, but not vice versa. In summary, this research pioneers in proposing a causal relationship between CD and acute pancreatitis among the European population.
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Colitis Ulcerosa , Enfermedad de Crohn , Estudio de Asociación del Genoma Completo , Análisis de la Aleatorización Mendeliana , Pancreatitis , Polimorfismo de Nucleótido Simple , Humanos , Colitis Ulcerosa/genética , Colitis Ulcerosa/epidemiología , Colitis Ulcerosa/complicaciones , Enfermedad de Crohn/genética , Enfermedad de Crohn/epidemiología , Pancreatitis/genética , Pancreatitis/epidemiología , Pancreatitis/etiología , Predisposición Genética a la Enfermedad , Factores de Riesgo , Enfermedad AgudaRESUMEN
Background: Whether machine learning (ML) can assist in the diagnosis of Crohn's disease (CD) and intestinal tuberculosis (ITB) remains to be explored. Methods: We collected clinical data from 241 patients, and 51 parameters were included. Six ML methods were tested, including logistic regression, decision tree, k-nearest neighbor, multinomial NB, multilayer perceptron, and XGBoost. SHAP and LIME were subsequently introduced as interpretability methods. The ML model was tested in a real-world clinical practice and compared with a multidisciplinary team (MDT) meeting. Results: XGBoost displays the best performance among the six ML models. The diagnostic AUROC and the accuracy of XGBoost were 0.946 and 0.884, respectively. The top three clinical features affecting our ML model's result prediction were T-spot, pulmonary tuberculosis, and onset age. The ML model's accuracy, sensitivity, and specificity in clinical practice were 0.860, 0.833, and 0.871, respectively. The agreement rate and kappa coefficient of the ML and MDT methods were 90.7% and 0.780, respectively (P<0.001). Conclusion: We developed an ML model based on XGBoost. The ML model could provide effective and efficient differential diagnoses of ITB and CD with diagnostic bases. The ML model performs well in real-world clinical practice, and the agreement between the ML model and MDT is strong.
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Pancreatic cancer (PC) is a malignant digestive system tumor with a very poor prognosis. N6-methyladenosine (m6A) is mediated by a variety of readers and participates in important regulatory roles in PC. Based on TCGA_PAAD, ICGC_AU_PAAD, ICGC_CA_PAAD, GSE28735 and GSE62452 datasets, We mapped the multi-omics changes of m6A readers in PC and found that m6A readers, especially IGF2BP family genes, had specific changes and were significantly associated with poor prognosis. An unsupervised consensus clustering algorithm was used to explore the correlation between specific expression patterns of m6A readers in PC and enrichment pathways, tumor immunity and clinical molecular subtypes. Then, the principal component analysis (PCA) algorithm was used to quantify specific expression patterns and screen core genes. Machine learning algorithms such as Bootstrapping and RSF were used to quantify the expression patterns of core genes and construct a prognostic scoring model for PC patients. What's more, pharmacogenomic databases were used to screen sensitive drug targets and small molecule compounds for high-risk PC patients in an all-around and multi-angle way. Our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy based on IGF2BP2-mediated m6A modification patterns.
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Background: COMMD10 has an important role in the development of certain tumors, but its relevance to gastric cancer (GC) is unclear. The purpose of this study is to investigate the difference of COMMD10 expression in gastric adenocarcinoma (STAD) and analyze the correlation between COMMD10 expression and prognosis of STAD patients. Methods: The expression levels of COMMD10 between STAD and normal tissues were explored using the The Cancer Genome Atlas (TCGA) database. In addition, the expression of COMMD10 in GC was further validated by immunohistochemistry (IHC) staining, qRT-PCR and Western blot. Dot blot experiments were used for exploring m6A expression levels in tissues with high and low COMMD10 expression. Kaplan-Meier analysis and COX regression analysis were used to explore the relationship between COMMD10 and STAD prognosis. A nomogram was constructed to predict the survival probability of STAD patients. GO and KEGG functional enrichment of COMMD10-related genes were performed. The Corrlot software package was used to analyze the correlation between COMMD10 expression levels and m6A modifications in STAD. An analysis of immune infiltration based on the CIBERSOFT and the single-sample GSEA (ssGSEA) method was performed. Results: COMMD10 expression was significantly associated with multiple cancers, including STAD in TCGA. COMMD10 expression was elevated in STAD cancer tissues compared to paracancerous tissues. COMMD10 upregulation was associated with poorer overall survival (OS), clinical stage, N stage, and primary treatment outcome in STAD. Functional enrichment of COMMD10-related genes was mainly involved in biological processes such as RNA localization, RNA splicing, RNA transport, mRNA surveillance pathways, and spliceosomes. The dot blot experiment showed that m6A levels were higher in cancer tissues with high COMMD10 expression compared with paracancerous tissues. COMMD10 was significantly correlated with most m6A-related genes. COMMD10 was involved in STAD immune cells infiltration, correlated with macrophage cells expression. Conclusion: High COMMD10 expression was significantly associated with poor prognosis in STAD patients, and its functional realization was related to m6A modification. COMMD10 involved in STAD immune infiltration.
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Adenocarcinoma , Neoplasias Gástricas , Humanos , Adenocarcinoma/genética , Biomarcadores , Western Blotting , Pronóstico , Neoplasias Gástricas/genéticaRESUMEN
Background: Accumulating evidence has shown that patients with inflammatory bowel disease (IBD) have liver function abnormalities and are susceptible to liver diseases. However, the existence of a causal relationship between IBD and liver function or disease remains unclear. Methods: A two-sample Mendelian randomization (MR) analysis was performed using genetic associations from publicly available genome-wide association studies (GWAS). These associations encompass ulcerative colitis (UC), Crohn's disease (CD), liver function traits, and liver disease phenotypes. The liver function traits comprised hepatic biochemistries, percent liver fat, and liver iron content from the UK Biobank. Furthermore, the liver disease phenotypes included cholelithiasis, non-alcoholic fatty liver disease (NAFLD), primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC) in cohorts of European ancestry. The primary estimation used the inverse-variance weighted method, with GWAS of C-reactive protein (CRP) in the UK Biobank serving as a positive control outcome. Results: Genetically predicted UC is causally associated with decreased levels of albumin (ALB) and liver iron content, while genetically predicted CD is causally associated with increased levels of alkaline phosphatase (ALP). Moreover, genetically predicted UC or CD increases the risk of PSC, and CD increases the risk of PBC. Neither UC nor CD causally increases the risk of cholelithiasis and NAFLD. Conclusion: UC affects the levels of ALB and liver iron content, while CD affects the levels of ALP. Both UC and CD increase the risk of PSC, and CD increases the risk of PBC.
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Background: Helicobacter pylori (H.pylori) infection is an important factor in the occurrence of human gastric diseases, but its pathogenic mechanism is not clear. N6-methyladenosine (m6A) is the most prevalent reversible methylation modification in mammalian RNA and it plays a crucial role in controlling many biological processes. However, there are no studies reported that whether H. pylori infection impacts the m6A methylation of stomach. In this study, we measured the overall level changes of m6A methylation of RNA under H. pylori infection through in vitro and in vivo experiment. Methods: The total quantity of m6A was quantified in gastric tissues of clinical patients and C57 mice with H. pylori infection, as well as acute infection model [H. pylori and GES-1 cells were cocultured for 48 h at a multiplicity of infection (MOI) from of 10:1 to 50:1]. Furthermore, we performed m6A methylation sequencing and RNA-sequencing on the cell model and RNA-sequencing on animal model. Results: Quantitative detection of RNA methylation showed that H. pylori infection group had higher m6A modification level. M6A methylation sequencing identified 2,107 significantly changed m6A methylation peaks, including 1,565 upregulated peaks and 542 downregulated peaks. A total of 2,487 mRNA was upregulated and 1,029 mRNA was downregulated. According to the comprehensive analysis of MeRIP-seq and RNA-seq, we identified 200 hypermethylation and upregulation, 129 hypermethylation but downregulation, 19 hypomethylation and downregulation and 106 hypomethylation but upregulation genes. The GO and KEGG pathway analysis of these differential methylation and regulatory genes revealed a wide range of biological functions. Moreover, combining with mice RNA-seq results, qRT- PCR showed that m6A regulators, METTL3, WTAP, FTO and ALKBH5, has significant difference; Two key genes, PTPN14 and ADAMTS1, had significant difference by qRT- PCR. Conclusion: These findings provide a basis for further investigation of the role of m6A methylation modification in H. pylori-associated gastritis.
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This study aimed to identify author, country, institutional, and journal collaborations and assess their impact, along with knowledge base, as well as identify existing trends, and uncover emerging topics related to matrix metalloproteinase and pancreatic-cancer research. A total of 1474 Articles and reviews were obtained from the Web of Science Core Collection and analyzed by Citespace and Vosviewer. CANCER RESEARCH, CLINICAL CANCER RESEARCH, and FRONTIERS IN IMMUNOLOGY are the most influential journals. The three main aspects of research in matrix metalloproteinases-pancreatic cancer-related fields included the pathogenesis mechanism of pancreatic cancer, how matrix metalloproteinases affect the metastasis of pancreatic cancer, and what role matrix metalloproteinases play in pancreatic cancer treatment. Tumor microenvironment, pancreatic stellate cells, drug resistance, and immune cells have recently emerged as research hot spots. In the future, exploring how immune cells affect matrix metalloproteinases and reshape the tumor microenvironment may be the key to curing pancreatic cancer. This study thus offers a comprehensive overview of the matrix metalloproteinases-pancreatic cancer-related field using bibliometrics and visual methods, providing a valuable reference for researchers interested in matrix metalloproteinases-pancreatic cancer.