A novel immune-related genes prognosis biomarker for hepatocellular carcinoma

An immune-related signature for optimizing prognosis prediction and treatment decision of hepatocellular carcinoma

BACKGROUND

An immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC).

METHODS

Based on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients.

RESULTS

The IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients.

CONCLUSIONS

This study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

Construction and validation of a novel coagulation-related 7-gene prognostic signature for gastric cancer

Background: Gastric cancer (GC) is the most common malignant tumor. Due to the lack of practical molecular markers, the prognosis of patients with advanced gastric cancer is still poor. A number of studies have confirmed that the coagulation system is closely related to tumor progression. Therefore, the purpose of this study was to construct a coagulation-related gene signature and prognostic model for GC by bioinformatics methods.

Methods: We downloaded the gene expression and clinical data of GC patients from the TCGA and GEO databases. In total, 216 coagulation-related genes (CRGs) were obtained from AmiGO 2. Weighted gene co-expression network analysis (WGCNA) was used to identify coagulation-related genes associated with the clinical features of GC. Last absolute shrinkage and selection operator (LASSO) Cox regression was utilized to shrink the relevant predictors of the coagulation system, and a Coag-Score prognostic model was constructed based on the coefficients. According to this risk model, GC patients were divided into high-risk and low-risk groups, and overall survival (OS) curves and receiver operating characteristic (ROC) curves were drawn in the training and validation sets, respectively. We also constructed nomograms for predicting 1-, 2-, and 3-year survival in GC patients. Single-sample gene set enrichment analysis (ssGSEA) was exploited to explore immune cells’ underlying mechanisms and correlations. The expression levels of coagulation-related genes were verified by real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results: We identified seven CRGs employed to construct a Coag-Score risk model using WGCNA combined with LASSO regression. In both training and validation sets, GC patients in the high-risk group had worse OS than those in the low-risk group, and Coag-Score was identified as an independent predictor of OS, and the nomogram provided a quantitative method to predict the 1-, 2-, and 3-year survival rates of GC patients. Functional analysis showed that Coag-Score was mainly related to the MAPK signaling pathway, complement and coagulation cascades, angiogenesis, epithelial–mesenchymal transition (EMT), and KRAS signaling pathway. In addition, the high-risk group had a significantly higher infiltration enrichment score and was positively associated with immune checkpoint gene expression. Conclusion: Coagulation-related gene models provide new insights and targets for the diagnosis, prognosis prediction, and treatment management of GC patients.

Liquid-Liquid Phase Separation-Related Genes Associated with Tumor Grade and Prognosis in Hepatocellular Carcinoma: A Bioinformatic Study

Aim

The aim of the present study was to identify the association between tumor grade and liquid-liquid phase separation (LLPS)-related genes, and to generate a LLPS-related gene-based risk index (LLPSRI) as a prognostic tool for hepatocellular carcinoma (HCC).

Methods

Weighted gene correlation network analysis was performed to test whether the LLPS-related gene modules were associated with tumor grade of HCC. The candidate modules were subjected to functional enrichment analysis. We generated a LLPSRI using the expression profiles of the hub genes among the candidate modules in order to identify patients at high risk. Then, the biological characteristics of the high-risk patients were revealed using gene set enrichment analysis. Additionally, an independent external data set was used to validate the LLPSRI.

Results

Four gene modules showed a significant positive correlation with tumor grade and involved various cancer-related pathways. Among the hub genes, six were selected to generate the LLPSRI, which was significantly associated with prognosis of HCC patients. The LLPSRI could successfully divide patients with HCC into high- and low-risk groups, and patients in the high-risk group showed shorter overall survival than those in the low-risk group. E2F, MYC, and mTORC1 signaling may be important determinants of survival in the high-risk group. The prognostic value of the LLPSRI was validated with the independent external data set.

Conclusion

We identified LLPS-related gene modules that are associated with HCC tumor grade. The LLPSRI may be useful as a prognostic marker of HCC, and it may reliably stratify patients into groups at low or high risk of worse survival. Our analysis also suggests that certain biological characteristics of HCC may be associated with high risk of worse survival.

A Novel Ferroptosis-Related 4-Gene Prognostic Signature for Cholangiocarcinoma and Photodynamic Therapy

Cholangiocarcinoma is the second most common malignant tumor in the hepatobiliary system. Compared with data on hepatocellular carcinoma, fewer public data and prognostic-related studies on cholangiocarcinoma are available, and effective prognostic prediction methods for cholangiocarcinoma are lacking. In recent years, ferroptosis has become an important subject of tumor research. Some studies have indicated that ferroptosis plays an important role in hepatobiliary cancers. However, the prediction of the prognostic effect of ferroptosis in patients with cholangiocarcinoma has not been reported. In addition, many reports have described the ability of photodynamic therapy (PDT), a potential therapy for cholangiocarcinoma, to regulate ferroptosis by generating reactive oxygen species (ROS). By constructing ferroptosis scores, the prognoses of patients with cholangiocarcinoma can be effectively predicted, and potential gene targets can be discovered to further enhance the efficacy of PDT. In this study, gene expression profiles and clinical information (TCGA, E-MTAB-6389, and GSE107943) of patients with cholangiocarcinoma were collected and divided into training sets and validation sets. Then, a model of the ferroptosis gene signature was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. Furthermore, through the analysis of RNA-seq data after PDT treatment of cholangiocarcinoma, PDT-sensitive genes were obtained and verified by immunohistochemistry staining and Western blot. The results of this study provide new insight for predicting the prognosis of cholangiocarcinoma and screening target genes that enhance the efficacy of PDT.

High S100A9+ cell density predicts a poor prognosis in hepatocellular carcinoma patients after curative resection

S100A9 is differentially expressed in various cell types and is associated with the development, progression and metastasis of various cancers. However, the expression, distribution, and clinical significance of S100A9 in hepatocellular carcinoma (HCC) remain unclear. In the present study, The Cancer Genome Atlas (TCGA) database was used to examine S100A9 gene expression in HCC; we found that S100A9 expression was associated with HCC prognosis. In addition, S100A9 protein expression was assessed by immunohistochemistry analysis of tissues from 382 HCC patients. We found that the infiltration of S100A9⁺ cells in both tumor and nontumor tissues could predict poor overall survival (P = 0.0329, tumor; P = 0.0003, nontumor) and a high recurrence risk (P = 0.0387, tumor; P = 0.0015, nontumor) in our tissue microarray analysis. Furthermore, immunofluorescence double staining revealed that the primary S100A9-expressing cells in adjacent nontumoral tissue were CD15⁺ neutrophils, and both CD68⁺ macrophages and CD15⁺ neutrophils expressed S100A9 in HCC tumor tissues. Taken together, the results suggest that high S100A9⁺ cell density predicts a poor prognosis in HCC patients, and S100A9 expression could potentially serve as an independent prognostic marker for HCC.

Novel Insight Into the Role of Immune Dysregulation in Amyotrophic Lateral Sclerosis Based on Bioinformatic Analysis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of motor neurons. The causative pathogenic mechanisms in ALS remain unclear, limiting the development of treatment strategies. Neuroinflammation and immune dysregulation were involved in the disease onset and progression of several neurodegenerative disorders, including ALS. In this study, we carried out a bioinformatic analysis using publicly available datasets from Gene Expression Omnibus (GEO) to investigate the role of immune cells and genes alterations in ALS. Single-sample gene set enrichment analysis revealed that the infiltration of multiple types of immune cells, including macrophages, type-1/17 T helper cells, and activated CD4 + /CD8 + T cells, was higher in ALS patients than in controls. Weighted gene correlation network analysis identified immune genes associated with ALS. The Gene Ontology analysis revealed that receptor and cytokine activities were the most highly enriched terms. Pathway analysis showed that these genes were enriched not only in immune-related pathways, such as cytokine-cytokine receptor interaction, but also in PI3K-AKT and MAPK signaling pathways. Nineteen immune-related genes (C3AR1, CCR1, CCR5, CD86, CYBB, FCGR2B, FCGR3A, HCK, ITGB2, PTPRC, TLR1, TLR2, TLR7, TLR8, TYROBP, VCAM1, CD14, CTSS, and FCER1G) were identified as hub genes based on least absolute shrinkage and selection operator analysis. This gene signature could differentiate ALS patients from non-neurological controls (p < 0.001) and predict disease occurrence (AUC = 0.829 in training set; AUC = 0.862 in test set). In conclusion, our study provides potential biomarkers of ALS for disease diagnosis and therapeutic monitoring.

Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population

BACKGROUND

Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers.

METHODS

First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples.

RESULTS

Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10^-6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets.

CONCLUSIONS

Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.