WGCNA, LASSO and SVM Algorithm Revealed RAC1 Correlated M0 Macrophage and the Risk Score to Predict the Survival of Hepatocellular Carcinoma Patients

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the "innate counterpart" of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

Eosinophil-Associated Genes are Potential Biomarkers for Hepatocellular Carcinoma Prognosis

Background: Eosinophils, a type of white blood cell originating from the bone marrow, are widely believed to play a crucial role in inflammatory processes, including allergic reactions and parasitic infections. However, the relationship between eosinophils and liver cancer is not well understood. Methods: Tumor immune infiltration scores were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Key modules and hub genes associated with eosinophils were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Univariate and multivariate Cox analyses, along with LASSO regression, were used to identify prognostic genes and create a risk model. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to evaluate the immunotherapeutic significance of the eosinophil-associated gene risk score (ERS) model. Experiments such as flow cytometry, immunohistochemical analysis, real-time quantitative PCR (RT-qPCR), and Western blotting were used to determine gene expression levels and the status of eosinophil infiltration in tumors. Results: A risk trait model including 4 eosinophil-associated genes (RAMP3, G6PD, SSRP1, PLOD2) was developed by univariate Cox analysis and Lasso screening. Pathologic grading (p < 0.001) and model risk scores (p < 0.001) were found to be independent predictors of hepatocellular carcinoma (HCC) patient survival. Western blotting revealed higher levels of eosinophil peroxidase (EPX) in HCC tissues compared to adjacent normal tissues. Immunohistochemistry showed that eosinophils mainly infiltrated the connective tissue around HCC. The HCC samples showed low expression of RAMP3 and high expression of G6PD, SSRP1, and PLOD2, as detected by IHC and RT-qPCR analysis. The in vivo mouse experiments showed that IL-33 treatment induced the recruitment of eosinophils and reduced the number of intrahepatic tumor nodules. Conclusion: Overall, eosinophil infiltration in HCC is significantly correlated with patient survival. The risk assessment model based on eosinophil-related genes serves as a reliable clinical prognostic indicator and provides insights for precise treatment of HCC.

Constructing a prognostic model for hepatocellular carcinoma based on bioinformatics analysis of inflammation-related genes

Background

This study aims to screen inflammation-related genes closely associated with the prognosis of hepatocellular carcinoma (HCC) to accurately forecast the prognosis of HCC patients.

Methods

Gene expression matrices and clinical information for liver cancer samples were obtained from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An intersection of differentially expressed genes of HCC and normal and GeneCards yielded inflammation-related genes associated with HCC. Cox regression and the minor absolute shrinkage and selection operator (LASSO) regression analysis to filter genes associated with HCC prognosis. The prognostic value of the model was confirmed by drawing Kaplan-Meier and ROC curves. Select differentially expressed genes between the high-risk and low-risk groups and perform GO and KEGG pathways analyses. CIBERSORT analysis was conducted to assess associations of risk models with immune cells and verified using real-time qPCR.

Results

A total of six hub genes (C3, CTNNB1, CYBC1, DNASE1L3, IRAK1, and SERPINE1) were selected using multivariate Cox regression to construct a prognostic model. The validation evaluation of the prognostic model showed that it has an excellent ability to predict prognosis. A line plot was drawn to indicate the HCC patients' survival, and the calibration curve revealed satisfactory predictability. Among the six hub genes, C3 and DNASE1L3 are relatively low expressed in HCCLM3 and 97H liver cancer cell lines, while CTNNB1, CYBC1, IRAK1, and SERPINE1 are relatively overexpressed in liver cancer cell lines.

Conclusion

One new inflammatory factor-associated prognostic model was constructed in this study. The risk score can be an independent predictor for judging the prognosis of HCC patients' survival.

A pyrimidine metabolism-related signature for prognostic and immunotherapeutic response prediction in hepatocellular carcinoma by integrating analyses

BACKGROUND

Hepatocellular carcinoma (HCC), with discouraging morbidity and mortality, ranks as one of the most prevalent tumors worldwide. Pyrimidine metabolism is a critical process that regulates DNA and RNA synthesis in cells. It is imperative to investigate the significance of pyrimidine metabolism in liver cancer.

METHODS

Transcriptome and clinical data were downloaded from the TCGA database and the GEO database. The genes related to pyrimidine metabolism were sourced from the MSigDB. The pyrimidine metabolism-related signature (PMRS) was constructed through Cox regression and Lasso regression and then verified in the external validation set from the ICGC database. Functional enrichment, immune infiltration analysis, drug sensitivity, and Immunophenoscore (IPS) were further implemented to predict the response to immunotherapy. The role of PMRS in the malignant phenotype of hepatocellular carcinoma was explored by conducting a series of in vitro experiments.

RESULTS

Our study developed a four-genes PMRS which demonstrates a substantial correlation with the prognosis of HCC patients, serving as an independent predictor in clinical practice. The result of risk-stratified analysis yielded evidence that low-risk patients experienced more favorable clinical outcomes. The nomogram exhibited remarkable prognostic predictive value. The subsequent results revealed that low-risk patients manifested a more promising response to immunotherapy. Moreover, the results of cell experiments demonstrated that the downregulation of DCK markedly inhibited the malignant phenotype of hepatocellular carcinoma.

CONCLUSIONS

Our pyrimidine metabolism-centered prognostic signature accurately predicts overall survival, immune status, and treatment response in hepatocellular carcinoma (HCC) patients, offering innovative insights for precise diagnosis, personalized treatment, and improved prognosis.

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Background

Calcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.

Methods

Three microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.

Results

We identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.

Conclusion

Three pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.

The role of m6A RNA methylation regulator in meningioma

Meningiomas are common intracranial tumors, and the effect of surgical resection is often unsatisfactory. N6-Methyladenosine (m6A)-related regulator expression levels are related to cancer occurrence and development. This study aimed to investigate the roles of m6A RNA methylation regulators in meningiomas, as these are currently unclear. Two m6A methylation-regulated genes (METTL3 and IGF2BP2) were identified as survival-associated linear models for RiskScore through bioinformatics analysis. Univariate and multivariate Cox regression analyses showed that the overall survival of patients with meningioma in the high-risk group was substantially shorter than that in the low-risk group. Weighted gene co-expression network analysis constructed a co-expression network based on the m6A methylation model (RiskScore). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses identified the biological processes of hub module gene behavior, and Cytoscape constructed an m6A methylation-related gene regulatory network. In vitro experiments verified that the mRNA and protein expression levels of METTL3 and IGF2BP2 were lower in meningioma cells than in normal meningioma cells. Therefore, central regulators of m6A methylation (METTL3 and IGF2BP2) could potentially serve as novel therapeutic targets in meningioma. Subsequently, a novel methylation signature (RiskScore) was developed for prognostic prediction in patients with meningioma.

Comprehensive analysis of immunogenic cell death associated genes expression, tumor microenvironment, and prognosis in hepatocellular carcinoma

Background: Immunogenic cell death (ICD) plays an important role in the development of cancers. This study attempted to explore the role of ICD in the prognosis of hepatocellular carcinoma (HCC).

Methods: Gene expression and clinical data were downloaded from The Cancer Genome Alas and Gene Expression Omnibus dataset. The immune/stromal/Estimate scores of the tumor microenvironment (TME) were calculated by ESTIMATE and CIBERSORT algorithms. Kaplan-Meier analysis, functional enrichment analysis, least absolute shrinkage and selection operator (LASSO) analysis, and univariate and multivariate Cox regression analysis were used for prognostic gene screening and prognostic model construction. The correlation of immune cell infiltration and risk scores was analyzed as well. Molecular docking was used to explore the relevance of related genes to anti-cancer drugs.

Results: Ten ICD associated differentially expressed genes in HCC were found, and all of them had good predictive ability for HCC. ICD gene high amount of expression group was associated with poor prognosis (p = 0.015). The TME, immune cell infiltration and gene expression were different between ICD high and low groups (all p &lt; 0.05). Six ICD associated genes (BAX, CASP8, IFNB1, LY96, NT5E and PIK3CA) which could predict the survival status were identified and used to construct the prognostic model for HCC. A risk score was calculated and it could be used as an independent prognostic factor in HCC patients (p &lt; 0.001). In addition, the risk score had a positive correlation with macrophage M0 (r = 0.33, p = 0.0086). Molecular docking indicated that sorafenib could bind strongly to the target protein, representing that sorafenib may exert anticancer effects through these six ICD associated genes.

Conclusion: This study established a prognostic model including six ICD associated genes for HCC, which may deepen our understanding of ICD and guide therapy for HCC patients.

Prognostic nomogram based on the lymph node metastasis indicators for patients with bladder cancer: A SEER population-based study and external validation

PURPOSE

This study aimed to compare the prognostic value of multiple lymph node metastasis (LNM) indicators and to develop optimal prognostic nomograms for bladder cancer (BC) patients.

METHODS

BC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly partitioned into training and internal validation cohorts. Genomic and clinical data were collected from The Cancer Genome Atlas (TCGA) as external validation cohort. The predictive efficiency of LNM indicators was compared by constructing multivariate Cox regression models. We constructed nomograms on basis of the optimal models selected for overall survival (OS) and cause-specific survival (CSS). The performance of nomograms was evaluated with calibration plot, time-dependent area under the curve (AUC) and decision curve analysis (DCA) in three cohorts. We subsequently estimated the difference of biological function and tumor immunity between two risk groups stratified by nomograms in TCGA cohort.

RESULTS

Totally, 10,093 and 107 BC patients were screened from the SEER and TCGA databases. N classification, positive lymph nodes (PLNs), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were all independent predictors for OS and CSS. The filtered models containing LODDS had minimal Akaike Information Criterion, maximal concordance indexes and AUCs. Age, LODDS, T and M classification were integrated into nomogram for OS, while nomogram for CSS included gender, tumor grade, LODDS, T and M classification. The nomograms were successfully validated in predictive accuracy and clinical utility in three cohorts. Additionally, the tumor microenvironment was different between two risk groups.

CONCLUSIONS

LODDS demonstrated superior prognostic performance over N classification, PLN and LNR for OS and CSS of BC patients. The nomograms incorporating LODDS provided appropriate prediction of BC, which could contribute to the tumor assessment and clinical decision-making.

A novel cuproptosis-related lncRNA signature predicts the prognosis and immunotherapy for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most serious malignant tumors with a poor prognosis worldwide. Cuproptosis is a novel copper-dependent cell death form, involving mitochondrial respiration and lipoylated components of the tricarboxylic acid (TCA) cycle. Long non-coding RNAs (lncRNAs) have been demonstrated to affect the tumorigenesis, growth, and metastasis of HCC.

OBJECTIVE

We explored the potential roles of cuproptosis-related lncRNAs in predicting the prognosis for HCC.

METHODS

The RNA-seq transcriptome data, mutation data, and clinical information data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analyses were performed to identify a prognostic cuproptosis-related lncRNA signature. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of the lncRNA signature for HCC. The enrichment pathways, immune functions, immune cell infiltration, tumor mutation burden, and drug sensitivity were also analyzed.

RESULTS

We constructed a prognostic model consisting of 8 cuproptosis-related lncRNAs for HCC. The patients were divided into high-risk group and low-risk group according to the riskscore calculated using the model. Kaplan-Meier analysis revealed that the high-risk lncRNA signature was correlated with poor overall survival [hazard ratio (HR) =1.009, 95% confidence interval (CI) = 1.002-1.015; p= 0.010)] of HCC. A prognostic nomogram incorporated the lncRNA signature and clinicopathological features were constructed and showed favorable performance for predicting prognosis of HCC patients. In addition, the most immune-related functions were significantly different between the high-risk and low-risk groups. Tumor mutation burden (TMB) and immune checkpoints were also expressed differently between the two risk groups. Finally, HCC patients with low-risk score were more sensitive to several chemotherapy drugs.

CONCLUSIONS

The novel cuproptosis-related lncRNA signature could be used to predict prognosis and evaluate the effect of chemotherapy for HCC.