Genetic Analysis of Platelet-Related Genes in Hepatocellular Carcinoma Reveals a Novel Prognostic Signature and Determines PRKCD as the Potential Molecular Bridge

Secreted protein TNA: a promising biomarker for understanding the adipose-bone axis and its impact on bone metabolism

BACKGROUND

Osteoporosis (OP) is a systemic bone disease characterized by reduced bone mass and deterioration of bone microstructure, leading to increased bone fragility. Platelets can take up and release cytokines, and a high platelet count has been associated with low bone density. Obesity is strongly associated with OP, and adipose tissue can influence platelet function by secreting adipokines. However, the biological relationship between these factors remains unclear.

METHODS

We conducted differential analysis to identify OP platelet-related plasma proteins. And, making comprehensive analysis, including functional enrichment, protein-protein interaction network analysis, and Friends analysis. The key protein, Tetranectin (TNA/CLEC3B), was identified through screening. Then, we analyzed TNA's potential roles in osteogenic and adipogenic differentiation using multiple RNA-seq data sets and validated its effect on osteoclast differentiation and bone resorption function through in vitro experiments.

RESULTS

Six OP-platelet-related proteins were identified via differential analysis. Then, we screened the key protein TNA, which was found to be highly expressed in adipose tissue. RNA-seq data suggested that TNA may promote early osteoblast differentiation. In vitro experiments showed that knockdown of TNA expression significantly increased the expression of osteoclast markers, thereby promoting osteoclast differentiation and bone resorption.

CONCLUSIONS

We identified TNA as a secreted protein that inhibits osteoclast differentiation and bone resorption. While, it potentially promoted early osteoblast differentiation from bioinformatic results. TNA may play a role in bone metabolism through the adipose-bone axis.

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment

Background

Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene signature (HPRGS) and explore its clinical application value.

Methods

TCGA-LIHC cohort was used for training, and the LIRI-JP cohort and HCC cDNA microarray were used for validation. Machine learning algorithms constructed a prognostic gene label for HCC. Kaplan-Meier (K-M), ROC curve, multiple analyses, algorithms, and online databases were used to analyze differences between high- and low-risk populations. A nomogram was constructed to facilitate clinical application.

Results

We identified 119 differential genes based on transcriptome sequencing data from five independent HCC cohorts, and 53 of these genes were associated with overall survival (OS). Using 101 machine learning algorithms, the 10 most prognostic genes were selected. We constructed an HCC HPRGS with four genes (SOCS2, LCAT, ECT2, and TMEM106C). Good predictive performance of the HPRGS was confirmed by ROC, C-index, and K-M curves. Mutation analysis showed significant differences between the low- and high-risk patients. The low-risk group had a higher response to transcatheter arterial chemoembolization (TACE) and immunotherapy. Treatment response of high- and low-risk groups to small-molecule drugs was predicted. Linifanib was a potential drug for high-risk populations. Multivariate analysis confirmed that HPRGS were independent prognostic factors in TCGA-LIHC. A nomogram provided a clinical practice reference.

Conclusion

We constructed an HPRGS for HCC, which can accurately predict OS and guide the treatment decisions for patients with HCC.

Exploring the potential mechanisms of Rehmannia glutinosa in treating sepsis based on network pharmacology

The present study utilized network pharmacology to identify therapeutic targets and mechanisms of Rehmannia glutinosa in sepsis treatment. RNA-sequencing was conducted on peripheral blood samples collected from 23 sepsis patients and 10 healthy individuals. Subsequently, the RNA sequence data were analyzed for differential expression. Identification of active components and their putative targets was achieved through the HERB and SwissTarget Prediction databases, respectively. Functional enrichment analysis was performed using GO and KEGG pathways. Additionally, protein-protein interaction networks were constructed and survival analysis of key targets was conducted. Single-cell RNA sequencing provided cellular localization data, while molecular docking explored interactions with central targets. Results indicated significant involvement of identified targets in inflammation and Th17 cell differentiation. Survival analysis linked several targets with mortality rates, while molecular docking highlighted potential interactions between active components and specific targets, such as rehmaionoside a with ADAM17 and rehmapicrogenin with CD81. Molecular dynamics simulations confirmed the stability of these interactions, suggesting Rehmannia glutinosa's role in modulating immune functions in sepsis.

Aspirin suppresses hepatocellular carcinoma progression by inhibiting platelet activity

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. Platelets (PLTs) are known to play a key role in the maintenance of liver homeostasis and the pathophysiological processes of a variety of liver diseases. Aspirin is the most classic antiplatelet agent. However, the molecular mechanism of platelet action and whether aspirin can affect HCC progression by inhibiting platelet activity need further study.

AIM

To explore the impact of the antiplatelet effect of aspirin on the development of HCC.

METHODS

Platelet-rich plasma, platelet plasma, pure platelet, and platelet lysate were prepared, and a coculture model of PLTs and HCC cells was established. CCK-8 analysis, apoptosis analysis, Transwell analysis, and real-time polymerase chain reaction (RT-PCR) were used to analyze the effects of PLTs on the growth, metastasis, and inflammatory microenvironment of HCC. RT-PCR and Western blot were used to detect the effects of platelet activation on tumor-related signaling pathways. Aspirin was used to block the activation and aggregation of PLTs both in vitro and in vivo, and the effect of PLTs on the progression of HCC was detected.

RESULTS

PLTs significantly promoted the growth, invasion, epithelial-mesenchymal transition, and formation of an inflammatory microenvironment in HCC cells. Activated PLTs promoted HCC progression by activating the mitogen-activated protein kinase/protein kinase B/signal transducer and activator of transcription three (MAPK/ AKT/STAT3) signaling axis. Additionally, aspirin inhibited HCC progression in vitro and in vivo by inhibiting platelet activation.

CONCLUSION

PLTs play an important role in the pathogenesis of HCC, and aspirin can affect HCC progression by inhibiting platelet activity. These results suggest that antiplatelet therapy has promising application prospects in the treatment and combined treatment of HCC.

Aspirin suppresses hepatocellular carcinoma progression by inhibiting platelet activity

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. Platelets (PLTs) are known to play a key role in the maintenance of liver homeostasis and the pathophysiological processes of a variety of liver diseases. Aspirin is the most classic antiplatelet agent. However, the molecular mechanism of platelet action and whether aspirin can affect HCC progression by inhibiting platelet activity need further study.

AIM

To explore the impact of the antiplatelet effect of aspirin on the development of HCC.

METHODS

Platelet-rich plasma, platelet plasma, pure platelet, and platelet lysate were prepared, and a coculture model of PLTs and HCC cells was established. CCK-8 analysis, apoptosis analysis, Transwell analysis, and real-time polymerase chain reaction (RT-PCR) were used to analyze the effects of PLTs on the growth, metastasis, and inflammatory microenvironment of HCC. RT-PCR and Western blot were used to detect the effects of platelet activation on tumor-related signaling pathways. Aspirin was used to block the activation and aggregation of PLTs both in vitro and in vivo, and the effect of PLTs on the progression of HCC was detected.

RESULTS

PLTs significantly promoted the growth, invasion, epithelial-mesenchymal transition, and formation of an inflammatory microenvironment in HCC cells. Activated PLTs promoted HCC progression by activating the mitogen-activated protein kinase/protein kinase B/signal transducer and activator of transcription three (MAPK/ AKT/STAT3) signaling axis. Additionally, aspirin inhibited HCC progression in vitro and in vivo by inhibiting platelet activation.

CONCLUSION

PLTs play an important role in the pathogenesis of HCC, and aspirin can affect HCC progression by inhibiting platelet activity. These results suggest that antiplatelet therapy has promising application prospects in the treatment and combined treatment of HCC.

Prediction of Prognosis in Patients with Sepsis Based on Platelet-Related Genes

The study aimed to develop a risk prognostic model using platelet-related genes (PRGs) to predict sepsis patient outcomes. Sepsis patient data from the Gene Expression Omnibus (GEO) database and PRGs from the Molecular Signatures Database (MSigDB) were analyzed. Differential analysis identified 1139 differentially expressed genes (DEGs) between sepsis and control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed enrichment in functions related to immune cell regulation and pathways associated with immune response and infectious diseases. A risk prognostic model was established using LASSO and Cox regression analyses, incorporating 10 PRGs selected based on their association with sepsis prognosis. The model demonstrated good stratification and prognostic effects, confirmed by survival and receiver operating characteristic (ROC) curve analyses. It served as an independent prognostic factor in sepsis patients. Further analysis using the CIBERSORT algorithm showed higher infiltration of activated natural killer (NK) cells and lower infiltration of CD8 T cells and CD4 T cells naïve in the high-risk group compared to the low-risk group. Additionally, expression levels of human leukocyte antigen (HLA) genes were significantly lower in the high-risk group. In conclusion, the 10-gene risk model based on PRGs accurately predicted sepsis patient prognosis and immune infiltration levels. This study provides valuable insights into the role of platelets in sepsis prognosis and diagnosis, offering potential implications for personalized treatment strategies.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning

Background

The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.

Methods

Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.

Results

Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.

Conclusion

In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

Platelet-related gene risk score: a predictor for pancreatic cancer microenvironmental signature, chemosensitivity and prognosis

Recent studies have indicated that platelets may play a role in the advancement of pancreatic cancer by supporting tumor growth and increasing resistance to chemotherapy. This study aims to develop a prognostic model for pancreatic cancer using a platelet-related gene risk score. Prognostic platelet-related genes (PRGs) were identified from public databases and analyzed using cluster analysis. We investigated the microenvironment signatures and gene mutation patterns across different PRG-based molecular subtypes of pancreatic cancer. A prognostic model based on PRGs was developed using LASSO-Cox Regression Analysis. Additionally, we examined the correlation between the risk score and tumor clinical characteristics, as well as drug sensitivity. Two molecular subtypes, cluster C1 and C2, were identified. Cluster C2 was associated with a poorer prognosis compared to Cluster C1. The C1 group exhibited higher scores for activated CD8+ T cells, central memory CD4+ T cells, and natural killer T cells. The C2 group demonstrated a higher frequency of gene mutations. We established and validated a novel prognostic prediction model and platelet-related gene risk score for pancreatic cancer. The risk score was positively correlated with T stage, N stage, and tumor grade, and it presented a significant prognostic value compared to other clinical factors. In conclusion, a novel prognostic prediction model focusing on platelet involvement in pancreatic cancer has been developed, offering potential benefits for future drug therapies and clinical prognostic assessments.

The pharmacological mechanism of chaihu-jia-longgu-muli-tang for treating depression: integrated meta-analysis and network pharmacology analysis

Aim: Chaihu-jia-Longgu-Muli-tang (CLM) is derived from "Shang Han Lun" and is traditionally prescribed for treating depression. However, there is still a lack of evidence for its antidepressant effects, and the underlying mechanism is also unclear. This study aimed to assess clinical evidence on the efficacy of CLM in patients with depression using a meta-analysis and to explore its underlying antidepressant molecular mechanisms via network pharmacology. Methods: Eight open databases were searched for randomized controlled trials (RCTs) comparing the effects of CLM alone or combined with serotonin-norepinephrine reuptake inhibitors (SNRIs) and selective serotonin reuptake inhibitors (SSRIs) in patients with depression, evaluating the total effective rate of the treatment group (CLM alone or combined with SSRIs/SNRIs) and the control group (SNRIs or SSRIs), and comparing changes in depression scale, anxiety scale, sleep scale, inflammation indicators and adverse effects. Subsequently, the active ingredients and target genes of CLM were screened through six databases. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network and topology analysis were performed. Finally, Molecular docking was applied to evaluate the binding affinity between components and predicted targets. Results: Twenty-four RCTs with a total of 2,382 patients were included. For the efficacy of antidepression and adverse effects, whether CLM alone or in combination with SSRIs/SNRIs, the treatment group has no inferior to that of the control group. Additionally, the intervention of CLM + SSRI significantly improved the symptoms of anxiety and insomnia, and reduced serum IL-6 and TNF-α levels. For network pharmacology, a total of 129 compounds and 416 intersection targets in CLM were retrieved. The interaction pathway between CLM and depression is mainly enriched in PI3K-Akt, JAK-STAT, and NF-κB signaling pathway, PIK3R1, MAPK3, and AKT1 may be the potential targets of Stigmasterol, β-stiosterol, coumestrol. Conclusion: Compared to SSRIs/SNRIs alone, CLM is more effective and safe in treating depression. It not only significantly alleviates depressive mood, but improves symptoms such as anxiety and insomnia, with fewer side effects, especially in combination with SSRI. Its antidepressant mechanism may be correlated with the regulation of the PI3K/Akt signaling pathway and inhibiting inflammatory response.

Characterization of platelet-related genes and constructing signature combined with immune-related genes for predicting outcomes and immunotherapy response in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a highly malignant subtype of non-small cell lung cancer with poor prognosis. Platelets are known to play a critical role in cancer development and progression, and recent studies suggest that they can also regulate immune response in tumors. However, the relationship between platelet-related genes (PRGs) and LUSC prognosis and tumor microenvironments remains unclear. In this study, we used multiple bioinformatics algorithms to identify 25 dysregulated PRGs that were significantly associated with LUSC prognosis. We found that PRGs were involved in multiple biological processes, particularly in the tumor microenvironment, and that platelet-related scores (PRS) were a risk factor. Additionally, we established a 6-gene prognostic signature combining PRGs and immune-related genes that accurately predicted outcomes and immunotherapy efficacy in LUSC patients. Our study provides a comprehensive analysis of the biological functions and potential therapeutic targets of PRGs in LUSC, which may inform the development of new treatments for this disease.

A novel NHEJ gene signature based model for risk stratification and prognosis prediction in hepatocellular carcinoma

BACKGROUND

Non-homologous DNA end joining (NHEJ) is the predominant DNA double-strand break (DSB) repair pathway in human. However, the relationship between NHEJ pathway and hepatocellular carcinoma (HCC) is unclear. We aimed to explore the potential prognostic role of NHEJ genes and to develop an NHEJ-based prognosis signature for HCC.

METHODS

Two cohorts from public database were incorporated into this study. The Kaplan-Meier curve, the Least absolute shrinkage and selection operator (LASSO) regression analysis, and Cox analyses were implemented to determine the prognostic genes. A NHEJ-related risk model was created and verified by independent cohorts. We derived enriched pathways between the high- and low-risk groups using Gene Set Enrichment Analysis (GSEA). CIBERSORT and microenvironment cell populations-counter algorithm were used to perform immune infiltration analysis. XRCC6 is a core NHEJ gene and immunohistochemistry (IHC) was further performed to elucidate the prognostic impact. In vitro proliferation assays were conducted to investigate the specific effect of XRCC6.

RESULTS

A novel NHEJ-related risk model was developed based on 6 NHEJ genes and patients were divided into distinct risk groups according to the risk score. The high-risk group had a poorer survival than those in the low-risk group (P < 0.001). Meanwhile, an obvious discrepancy in the landscape of the immune microenvironment also indicated that distinct immune status might be a potential determinant affecting prognosis as well as immunotherapy reactiveness. High XRCC6 expression level associates with poor outcome in HCC. Moreover, XRCC6 could promote HCC cell proliferation in vitro.

CONCLUSIONS

In brief, this work reveals a novel NHEJ-related risk signature for prognostic evaluation of HCC patients, which may be a potential biomarker of HCC immunotherapy.