Identification of pivotal genes and crucial pathways in liver fibrosis through WGCNA analysis.

BACKGROUND: Liver fibrosis is a progressive liver disease with increasing incidence, yet its underlying pathogenic mechanisms remain incompletely understood. OBJECTIVE: : This study aims to explore potential therapeutic targets for liver fibrosis using weighted gene co-expression network analysis (WGCNA) and experimental validation. METHODS: We retrieved the microarray data (GSE174099) from the GEO database and performed differential expression analysis and WGCNA to identify co-expression modules associated with liver fibrosis. A module with the highest correlation to liver fibrosis was selected for further analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to investigate the biological functions and signaling pathways of the identified genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The correlation between core genes and immune cells was analyzed with the CIBERSORT algorithm. Additionally, pathological and molecular biology experiments were performed to validate the expression levels of core genes in liver tissue, including HE and Masson staining, immunohistochemistry, RT-qPCR, and Western blotting. RESULTS: We identified a total of 86 intersecting genes from the differential expression analysis and WGCNA. GO enrichment analysis revealed that these genes were involved in processes such as cellular response to cAMP, collagen-containing extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis highlighted the involvement of these genes in pathways like Cell Adhesion Molecules and the PI3K-Akt signaling pathway. Using Cytoscape software, we identified four core genes: Cftr, Cldn4, Map2, and Spp1. Pathological examinations showed that the experimental group exhibited significant fibrous tissue proliferation compared to the control group. Immunohistochemistry, RT-qPCR, and Western blotting analyses confirmed that these core genes were significantly upregulated in the experimental group (P< 0.05). CONCLUSION: This study identified four key genes (Cftr, Cldn4, Map2, Spp1) that are significantly associated with liver fibrosis. These genes are upregulated in liver fibrosis and could potentially as biomarkers for diagnosis and targets for therapeutic interventions.

Methylated SEPT9 combined with AFP and PIVKA-II is effective for the detection of HCC in high-risk population.

BACKGROUND: The methylation SEPT9 (mSEPT9) appeared to be effective for hepatocellular carcinoma (HCC) detection. However, its performance in high-risk population has not been validated. We designed a pilot study and aimed to investigate the performance of mSEPT9, AFP, PIVKA-II and their combination in hepatic cirrhosis (HC) population. METHODS: A training cohort was established including 103 HCC and 114 HC patients. 10 ml blood was collected from each patient with K2EDTA tubes, and 3-4 ml plasma was extracted for subsequent tests. The performance of mSEPT9, AFP, PIVKA-II and their combination was optimized by the training cohort. Test performance was prospectively validated with a validation cohort, including 51 HCC and 121 HC patients. RESULTS: At the optimal thresholds in the training cohort, the sensitivity, specificity and area under curve (AUC) was 72.82%, 89.47%, 0.84, and 48.57%, 89.92%, 0.79, and 63.64%, 95.95%, 0.79 for mSEPT9, AFP and PIVKA-II, respectively. The combined test significantly increased the sensitivity to 84.47% (P < 0.05) at the specificity of 86.84% with an AUC of 0.91. Stage-dependent performance was observed with all single markers and their combination in plasma marker levels, positive detection rate (PDR) and AUC. Moderate correlation was found between mSEPT9 and AFP plasma levels (r = 0.527, P < 0.0001). Good complementarity was found between any two of the three markers, providing optimal sensitivity in HCC detection when used in combination. Subsequent validation achieved a sensitivity, specificity and AUC of 65.31%, 92.86%, 0.80, and 44.24%, 89.26%, 0.75, and 62.22%, 95.27%, 0.78 for mSEPT9, AFP and PIVKA-II, respectively. The combined test yielded a significantly increased sensitivity of 84.00% (P < 0.05) at 85.57% specificity, with an AUC at 0.89. CONCLUSIONS: The performance was optimal by the combination of mSEPT9, AFP, PIVKA-II compared with any single marker, and the combination may be effective for HCC opportunistic screening in HC population.

Identification of hub genes and therapeutic drugs in osteonecrosis of the femoral head through integrated bioinformatics analysis and literature mining.

Osteonecrosis of the femoral head (ONFH) is a multifactorial disease leading to severely limited function. By far, the etiology and pathogenesis of ONFH are not fully understood, and surgery is the only effective way to treat ONFH. This study aims to identify hub genes and therapeutic drugs in ONFH. Two gene expression profiles were downloaded from the gene expression omnibus database, and the hub genes and candidate drugs for ONFH were identified through integrated bioinformatics analysis and cross-validated by literature mining. A total of 159 DEGs were identified. PTGS2, LRRK2, ANXA5, IGF1R, MCL1, TIMP2, LYN, CD68, CBL, and RUNX2 were validated as 10 hub genes, which has considerable implications for future genetic research and related research fields of ONFH. Our findings indicate that 85 drugs interact with ONFH, with most drugs exhibiting a positive impact on ONFH by promoting osteogenesis and angiogenesis or inhibiting microcirculation embolism, rather than being anti-inflammatory. Our study provides novel insights into the pathogenesis, prevention, and treatment of ONFH.

Analysis of angiogenesis-related subtypes of hepatocellular carcinoma and tumor microenvironment infiltration feature in hepatocellular carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC) is a highly vascularized tumor, and angiogenesis plays an important role in its progression. However, the role of angiogenesis in cell infiltration in the tumor microenvironment (TME) remains unclear. METHODS: We evaluated the associations of 35 angiogenesis-related genes (ARGs) with the clinicopathological features of 816 HCC patients. In addition, we assessed the associations between the ARGs and TME cell infiltration. A nomogram was constructed to determine the prognostic value of ARGs for HCC. The ARG score was used to distinguish angiogenic subtypes of HCC, and its usefulness for predicting the prognosis and treatment response of HCC patients was evaluated. RESULTS: We distinguished three ARG clusters differing in terms of TME cell infiltration, immune cell activation status, clinicopathological features, and clinical outcomes. There were significant associations of ARG expression with tumor immunity, the epithelial-mesenchymal transition (EMT), and transforming growth factor-ß expression. An ARG score model was constructed to generate a risk score for each patient based on differentially expressed genes between clusters. Furthermore, a high ARG score was associated with high expression of CTLA-4 and PD-L1/PD-1, and a low Tumor Immune Dysfunction and Exclusion score, indicating the usefulness of the ARG score for selecting patients for immunotherapy. Considering the relationship between ARGs and tumor immunity, immunotherapy combined with vascular-targeted therapy may be the best treatment for HCC. CONCLUSIONS: ARGs play an important role in TME diversity and complexity in HCC patients. The ARG score of HCC predicts TME invasion and can guide immunotherapy.

Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Hepatocellular Carcinoma.

BACKGROUND: Liver cancer is a major medical problem because of its high morbidity and mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Currently, the mechanism of HCC is unclear, and the prognosis is poor with limited treatment. OBJECTIVE: The purpose of this study is to identify hub genes and potential therapeutic drugs for HCC. METHODS: We used the GEO2R algorithm to analyze the differential expression of each gene in 4 gene expression profiles (GSE101685, GSE62232, GSE46408, and GSE45627) between HCC and normal hepatic tissues. Next, we screened out the differentially expressed genes (DEGs) by corresponding calculation data according to adjusted P-value < 0.05 and | log fold change (FC) | > 1.0. Subsequently, we used the DAVID software to analyze the DEGs by GO and KEGG enrichment analysis. Then, we carried out the protein-protein interaction (PPI) network analysis of DEGs using the STRING tool, and the PPI network was constructed by Cytoscape software. MCODE plugin was used for module analysis, and the hub genes were screened out by the Cyto- Hubba plugin. Meanwhile, we used The Kaplan-Meier plotter, GEPIA2 and HPA databases to exert survival analysis and verify the expression alternation of hub genes. Furthermore, we used ENCORI, TargetScan, miRDB and miRWalk database to predict the upstream regulated miRNA of hub genes and construct a miRNA-hub genes network by Cytoscape software. Finally, we selected potential therapeutic drugs for HCC through DGIdb databases. RESULTS: A total of 415 DEGs were screened in HCC, including 196 up-regulated DEGs and 219 down-regulated DEGs. The results of KEGG pathway analysis suggested that the up-regulated DEGs can regulate the cell cycle, and DNA replication signal pathway, while the down-regulated DEGs were associated with metabolic pathways. In this study, we identified 11 hub genes (AURKA, BUB1B, TOP2A, MAD2L1, CCNA2, CCNB1, BUB1, KIF11, CDK1, CCNB2 and TPX2), which were independent risk factors of HCCand all up-regulated DEGs. We verified the expression difference of hub genes through the GEPIA2 and HPA database, which was consistent with the results of GEO data. We found that those hub genes were mutations in HCC according to the cBioPortal database. Finally, we used the DGIdb database to select 32 potential therapeutic targeting drugs for hub genes. CONCLUSION: In summary, our study provided a new perspective for researching the molecular mechanism of HCC. Hub genes, miRNAs, and candidate drugs provide a new direction for the early diagnosis and treatment of HCC.

CD97 serves as a novel biomarker of immune cell infiltration in hepatocellular carcinoma.

BACKGROUND: CD97 is the most widely expressed G protein-coupled receptor in the epidermal growth factor seven-span transmembrane family. It plays a vital role in cell adhesion, migration, and cell connection regulation. We explored the role of CD97 in hepatocellular carcinoma (HCC). METHODS: We evaluated CD97 mRNA expression in HCC using TNMplot and the Gene Expression Omnibus database. The clinical prognostic significance of CD97 in HCC patients was evaluated by gene expression profiling interactive analysis, the Kaplan-Meier plotter, and the UALCAN database. The Tumor Immune Estimation Resource (TIMER) and CIBERSORT databases were used to analyze the relationships among CD97, genes positively related with CD97, and tumor-infiltrating immune cells. RESULTS: CD97 was highly expressed in HCC tissues and was associated with an adverse prognosis. CD97 and genes positively related with CD97 were positively correlated with the abundance of tumor-infiltrating immune cells and strongly correlated with tumor-infiltrating macrophages (all r ≥ 0.513, P < 0.001). CD97 was positively correlated with M2 macrophage and tumor-associated macrophage markers (both r ≥ 0.464, P < 0.001). CD97 was found to be an immune-related gene in HCC and positively correlated with the TOX, PD-L1, PD-L2, CTLA4, and PD-1 immune checkpoint genes. CD97 copy number alterations affect the level of immune cell infiltration and mRNA expression. CONCLUSIONS: CD97 can be used as a potential molecular marker of prognosis in HCC, which is associated with immune cell infiltration.

Analysis and identification of potential key genes in hepatic ischemia-reperfusion injury.

Background: Hepatic ischemia-reperfusion injury (HIRI) is an unavoidable surgical complication after liver transplantation, but current HIRI treatments cannot achieve satisfactory clinical outcomes. Thus, safer and more effective prevention and treatment methods need to be explored. Methods: Transcriptome messenger ribonucleic acid (mRNA) and long non-coding RNA (lncRNA) sequencing data were obtained from male Sprague-Dawley rats, and these data were used to identify the differentially expressed genes (DEGs) and differentially expressed lncRNAs (DE-lncRNAs) between the HIRI and control samples. A protein-protein interaction (PPI) network was also constructed for the DE-mRNAs to identify candidate genes, and the receiver operating characteristic curves of the 21 candidate genes were plotted to evaluate the diagnostic value of the candidate genes for HIRI. A random forest (RF) model, support vector machine model and generalized linear model were constructed based on the candidate genes. A gene set enrichment analysis (GSEA) of the key genes was conducted to determine the enriched pathways in the high expression groups. The miRWalk and miRanda database were used to constructed the lncRNA-miRNA-mRNA network. Finally, the expressions of the key genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 256 DEGs and 67 DE-lncRNAs were identified in the HIRI and control samples. To explore the interactions between the DE-mRNAs, a PPI network of 130 DEGs was constructed. Further, 21 genes were selected as the candidate genes. Subsequently, 6 genes [i.e., Keratin-14 (Krt14), Uroplakin 3B (Upk3b), Keratin 7 (Krt7), Cadherin 3 (Cdh3), mesothelin (Msln), and Glypican 3 (Gpc3)] in the RF model were defined as the key genes. The GSEA results indicated that these key genes were enriched in the terms of extracellular structure organization, and extracellular matrix organization. Moreover, a lncRNA-miRNA-mRNA network was constructed with 4 lncRNAs, 5 mRNAs, and 11 miRNAs. Finally, the results indicated that the expression of Krt14, Upk3b, Msln, and Gpc3 were more highly expressed in the control samples than the HIRI samples. Conclusions: A total of 6 key genes (i.e., Krt14, Upk3b, Krt7, Cdh3, Msln, and Gpc3) were identified. Our findings provide novel ideas for the diagnosis and treatment of HIRI.

Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study.

BACKGROUND: Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. METHODS: Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. RESULTS: To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19-9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19-9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. CONCLUSIONS: The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

Exosome-mediated peritoneal dissemination in gastric cancer and its clinical applications.

The prognosis of patients with peritoneal dissemination from gastric cancer is poor, and the underlying molecular mechanism remains unclear. Exosomes, as macromolecular phospholipid bilayer vesicles comprising of proteins, nucleic acids and lipids, serve as mediators of cell-cell communication. Gastric cancer tumor-derived exosomes may be involved in the pathological process of peritoneal dissemination by mediating crosstalk between cancer cells and mesothelial cells, to result in the induction of enhanced tumor growth, migratory, adhesive and invasive abilities, peritoneal fibrosis and apoptosis, mesothelial-to-mesenchymal transition, angiogenesis and chemoresistance. The present review focuses on previous studies addressing the exosome-dependent molecular transfer in peritoneal dissemination in gastric cancer and the potential clinical applications.