Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Myricetin Induces Ferroptosis and Inhibits Gastric Cancer Progression by Targeting NOX4

Ferroptosis holds great potential as a therapeutic approach for gastric cancer (GC), a prevalent and deadly malignant tumor associated with high rates of incidence and mortality. Myricetin, well-known for its multifaceted biomedical attributes, particularly its anticancer properties, has yet to be thoroughly investigated regarding its involvement in ferroptosis. The aim of this research was to elucidate the impact of myricetin on ferroptosis in GC progression. The present study observed that myricetin could trigger ferroptosis in GC cells by enhancing malondialdehyde production and Fe2+ accumulation while suppressing glutathione levels. Mechanistically, myricetin directly interacted with NADPH oxidase 4 (NOX4), influencing its stability by inhibiting its ubiquitin degradation. Moreover, myricetin regulated the inhibition of ferroptosis induced by Helicobacter pylori cytotoxin-associated gene A (CagA) through the NOX4/NRF2/GPX4 pathway. In vivo experiments demonstrated that myricetin treatment significantly inhibited the growth of subcutaneous tumors in BALB/c nude mice. It was accompanied by increased NOX4 expression in tumor tissue and suppression of the NRF2/GPX4 antioxidant pathway. Therefore, this research underscores myricetin as a novel inducer of ferroptosis in GC cells through its interaction with NOX4. It is a promising candidate for GC treatment.

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

The putative role of ferroptosis in gastric cancer: a review

Ferroptosis is a unique cell death modality triggered by iron-dependent lipid peroxidation, with cysteine metabolism and glutathione-dependent antioxidant defence responses as the primary triggering mechanisms. Ferroptosis is an independent tumour suppression mechanism and has been implicated in various disorders. In tumourigenesis, ferroptosis plays a dual role in promoting and inhibiting tumours. P53, NFE2L2, BAP1, HIF, and other tumour suppressor genes regulate ferroptosis, releasing damage-associated molecular patterns or lipid metabolites to influence cellular immune responses. Ferroptosis is also involved in tumour suppression and metabolism. The combination of amino acid, lipid, and iron metabolism is involved in the initiation and execution of ferroptosis, and metabolic regulatory mechanisms also play roles in malignancies. Most investigations into ferroptosis in gastric cancer are concentrated on predictive models, not the underlying processes. This review investigates the underlying mechanisms of ferroptosis, tumour suppressor genes, and the tumour microenvironment.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

Comprehensive analysis of disulfidptosis related genes and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is a common malignant tumor of the digestive system. Disulfidptosis is a new programmed cell death mechanism, although its specific mechanism in GC is incompletely understood.

AIM

In this study, we used bioinformatics analysis to explore a disulfidptosis-based predictive model related to GC prognosis and to identify potential therapeutic targets and sensitive drugs for GC.

METHODS

We extracted GC-related data from The Cancer Genome Atlas and Gene Expression Omnibus databases. R software (version 4.2.1) was used for correlation analysis.

RESULTS

Through the above analysis, we found that the disulfidptosis related gene may be related to the prognosis of GC. Six genes, namely, PLS3, GRP, APOD, SGCE, COL8A1, and VAMP7, were found to constitute a predictive model for GC prognosis. APOD is a potential therapeutic target for treating GC. Bosutinib and other drugs are sensitive for the treatment of GC.

CONCLUSION

The results of this study indicate that disulfidptosis is related to the prognosis and treatment of GC, while APOD represents a potential therapeutic target for GC.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.

Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.

Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p < 0.001, HR: 3.81, 95% CI: 2.44–5.956) and GSE15459 (p < 0.001, HR: 2.65, 95% CI: 1.892–3.709) cohorts and the external validation GSE84437 cohort (p < 0.001, HR: 2.06, 95% CI: 1.554–2.735). The nomogram constructed based on two independent predictive factors, tumor stage and the signature, predicted events tightly consistent with the actual (Hosmer–Lemeshow p value: 1-year, 0.624; 3-years, 0.795; 5-years, 0.824). For the molecular features, we observed the activation of apical junction, epithelial mesenchymal transition, and immune pathways in the high-risk group, while in the low-risk group, cell cycle associated G2M, E2F and MYC target pathways were activated. Based on the results we obtained, we indicated that gastric patients in the low-risk group are more suitable for 5-fluorouracil therapy, while high-risk group patients are more suitable for anti-CTLA4 immunotherapy, these results need more support in the further studies. After compare with proposed molecular subtypes, we realized that the nine-consensus prognostic gene signature is a powerful addition to identify the gastric patients with poor prognosis.

Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.

Establishment of a Novel Prognostic Prediction Model for Gastric Cancer Based on Necroptosis-Related Genes

Background: Necroptosis plays a crucial role in the progression of multiple types of cancer. However, the role of necroptosis in gastric cancer (GC) remains unclear. The aim of this study is to establish a necroptosis-related prediction model, which could provide information for treatment monitoring.

Methods: The TCGA-STAD cohort was employed to establish a prognostic prediction signature and the GEO dataset was employed for external validation. The correlation between the risk score and the immune landscape, tumor mutational burden (TMB), microsatellite instability (MSI), as well as therapeutic responses of different therapies were analyzed.

Results: We constructed a prognostic model based on necroptosis-associated genes (NAGs), and its favorable predictive ability was confirmed in an external cohort. The risk score was confirmed as an independent determinant, and a nomogram was further established for prognosis. A high score implies higher tumor immune microenvironment (TIME) scores and more significant TIME cell infiltration. High-risk patients presented with lower TMB, and low-TMB patients had worse overall survival (OS). Meanwhile, Low-risk scores are characterized by MSI-high (MSI-H), lower Tumor Immune Dysfunction and Exclusion (TIDE) score, and higher immunogenicity in immunophenoscore (IPS) analysis.

Conclusion: The developed NAG score provides a novel and effective method for predicting the outcome of GC as well as potential targets for further research.

Identification of immune-related ferroptosis prognostic marker and in-depth bioinformatics exploration of multi-omics mechanisms in thyroid cancer

Background: Factors such as variations in thyroid carcinoma (THCA) gene characteristics could influence the clinical outcome. Ferroptosis and immunity have been verified to play an essential role in various cancers, and could affect the cancer patients’ prognosis. However, their relationship to the progression and prognosis of many types of THCA remains unclear.

Methods: First, we extracted prognosis-related immune-related genes and ferroptosis-related genes from 2 databases for co-expression analysis to obtain prognosis-related differentially expressed immune-related ferroptosis genes (PR-DE-IRFeGs), and screened BID and CDKN2A for building a prognostic model. Subsequently, multiple validation methods were used to test the model’s performance and compare its performance with other 4 external models. Then, we explored the mechanism of immunity and ferroptosis in the occurrence, development and prognosis of THCA from the perspectives of anti-tumor immunity, CDKN2A-related competitive endogenous RNA regulatory, copy number variations and high frequency gene mutation. Finally, we evaluated this model’s clinical practice value.

Results: BID and CDKN2A were identified as prognostic risk and protective factors, respectively. External data and qRT-PCR experiment also validated their differential expression. The model’s excellent performance has been repeatedly verified and outperformed other models. Risk scores were significantly associated with most immune cells/functions. Risk score/2 PR-DE-IRFeGs expression was strongly associated with BRAF/NRAS/HRAS mutation. Single copy number deletion of CDKN2A is associated with upregulation of CDKN2A expression and worse prognosis. The predicted regulatory network consisting of CYTOR, hsa-miRNA-873-5p and CDKN2A was shown to significantly affect prognosis. The model and corresponding nomogram have been shown to have excellent clinical practice value.

Conclusion: The model can effectively predict the THCA patients’ prognosis and guide clinical treatment. Ferroptosis and immunity may be involved in the THCA’s progression through antitumor immunity and BRAF/NRAS/HRAS mutation. CYTOR-hsa-miRNA-873-5p-CDKN2A regulatory networks and single copy number deletion of CDKN2A may also affect THCA′ progression and prognosis.

Role of ferroptosis and ferroptosis-related non-coding RNAs in the occurrence and development of gastric cancer

Gastric cancer (GC) is a malignant cancer of the digestive tract and is a life-threatening disease worldwide. Ferroptosis is a newly discovered form of regulated cell death, which involves the accumulation of iron-dependent lipid peroxides. It has been found that ferroptosis plays an important regulatory role in the occurrence, development, drug resistance, and prognosis of GC. Non-coding RNAs (ncRNAs) play a critical role in the occurrence and progression of a variety of diseases including GC. In recent years, the role of ferroptosis and ferroptosis-related ncRNAs (miRNA, lncRNA, and circRNA) in the occurrence, development, drug resistance, and prognosis of GC has attracted more and more attention. Herein, we briefly summarize the roles and functions of ferroptosis and ferroptosis-related ncRNAs in GC tumorigenesis, development, and prognosis. We also prospected the future research direction and challenges of ferroptosis and ferroptosis-related ncRNAs in GC.

Construction of a survival nomogram for gastric cancer based on the cancer genome atlas of m6A-related genes

Objective: Based on TCGA database, a prediction model for 1-, 3-, and 5-year overall survival rates of gastric cancer (GC) patients was constructed by analyzing the critical risk factors affecting the prognosis of gastric cancer patients.

Method: Clinicopathological features as well as gene signature of GC patients were obtained from TCGA database. Patients were randomly divided into a training cohort and an internal validation cohort. Independent predictors of GC prognosis were analyzed by univariate and multivariate Cox analyses to construct nomogram. The accuracy and reliability of the model was further validated by calibration curves, ROC curves, and C-indexes, and the clinical utility of the model was analyzed by decision analysis curves.

Result: Age, sex, N stage, M stage, METTL16, RBM15, FMR1, IGFBP1, and FTO were significantly associated with the prognosis of GC patients, and these predictors were further included in the construction of nomogram. The C-indexes for the training cohort and validation set were 0.735 and 0.688, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) exceeded 0.6 in training and validation sets at 1, 3, and 5 years.

Conclusion: We have constructed and validated a nomogram that provides individual survival condition prediction for GC patients. The prognostic model integrating gene signatures and clinicopathological characteristics would help clinicians determine the prognosis of patients with GC and develop individualized treatment plans.

Identification of Hypoxia-Related Subtypes, Establishment of Prognostic Models, and Characteristics of Tumor Microenvironment Infiltration in Colon Cancer

Background: Immunotherapy is a treatment that can significantly improve the prognosis of patients with colon cancer, but the response to immunotherapy is different in patients with colon cancer because of the heterogeneity of colon carcinoma and the complex nature of the tumor microenvironment (TME). In the precision therapy mode, finding predictive biomarkers that can accurately identify immunotherapy-sensitive types of colon cancer is essential. Hypoxia plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism, and chemotherapy and immunotherapy resistance. Thus, understanding the mechanism of hypoxia-related genes (HRGs) in colon cancer progression and constructing hypoxia-related signatures will help enrich our treatment strategies and improve patient prognosis.

Methods: We obtained the gene expression data and corresponding clinical information of 1,025 colon carcinoma patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct hypoxia subtypes (subtype A and subtype B) according to unsupervised clustering analysis and assessed the clinical parameters, prognosis, and TME cell-infiltrating characteristics of patients in the two subtypes. We identified 1,132 differentially expressed genes (DEGs) between the two hypoxia subtypes, and all patients were randomly divided into the training group (n = 513) and testing groups (n = 512). Following univariate Cox regression with DEGs, we construct the prognostic model (HRG-score) including six genes (S1PR3, ETV5, CD36, FOXC1, CXCL10, and MMP12) through the LASSO–multivariate cox method in the training group. We comprehensively evaluated the sensitivity and applicability of the HRG-score model from the training group and the testing group, respectively. We explored the correlation between HRG-score and clinical parameters, tumor microenvironment, cancer stem cells (CSCs), and MMR status. In order to evaluate the value of the risk model in clinical application, we further analyzed the sensitivity of chemotherapeutics and immunotherapy between the low-risk group and high-risk group and constructed a nomogram for improving the clinical application of the HRG-score.

Result: Subtype A was significantly enriched in metabolism-related pathways, and subtype B was significantly enriched in immune activation and several tumor-associated pathways. The level of immune cell infiltration and immune checkpoint-related genes, stromal score, estimate score, and immune dysfunction and exclusion (TIDE) prediction score was significantly different in subtype A and subtype B. The level of immune checkpoint-related genes and TIDE score was significantly lower in subtype A than that in subtype B, indicating that subtype A might benefit from immune checkpoint inhibitors. Finally, an HRG-score signature for predicting prognosis was constructed through the training group, and the predictive capability was validated through the testing group. The survival analysis and correlation analysis of clinical parameters revealed that the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. There were also significant differences in immune status, mismatch repair status (MMR), and cancer stem cell index (CSC), between the two risk groups. The correlation analysis of risk scores with IC₅₀ and IPS showed that patients in the low-risk group had a higher benefit from chemotherapy and immunotherapy than those in the high-risk group, and the external validation IMvigor210 demonstrated that patients with low risk were more sensitive to immunotherapy.

Conclusion: We identified two novel molecular subgroups based on HRGs and constructed an HRG-score model consisting of six genes, which can help us to better understand the mechanisms of hypoxia-related genes in the progression of colon cancer and identify patients susceptible to chemotherapy or immunotherapy, so as to achieve precision therapy for colon cancer.

Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma

OBJECTIVE

Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity-related subtypes of hepatocellular carcinoma (HCC) have not been identified.

METHODS

Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8-gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis.

RESULTS

C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low-risk group, the high-risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response.

CONCLUSION

We identified HCC subtypes from the perspective of metabolism-related pathway activity and proposed a robust prognostic signature for HCC.

A Five-Gene Signature Associated With DNA Damage Repair Molecular Subtype Predict Overall Survival for Hepatocellular Carcinoma

Background: DNA damage repair (DDR) is an important mechanism for the occurrence and development of hepatocellular carcinoma (HCC), but its impact on prognosis has not been fully understood.

Materials and methods: A total of 904 HCC patients were included in our study, TCGA (n = 370) and GSE14520 (n = 239) were merged into a large-sample training cohort (n = 609). The training cohort was clustered into C1 and C2 based on prognostic DDR-related genes, the differentially expressed genes (DEGs) between C1 and C2 were identified by the Wilcoxon signed-rank test referred to criteria (|log2FC|≥1 and FDR&lt; 0.05). The univariate Cox analysis was used to screen the prognostic-related DEGs, and Lasso penalized Cox regression analysis was used to construct the risk score. The patients were clarified into high- and low-risk groups based on the median risk score. ICGC (n = 231) and GSE116174 (n = 64) cohorts were used for external validation of the risk score’s prognostic value.

Results: The Kaplan–Meier survival analysis showed that the high-risk group had a significantly reduced overall survival (OS) compared to the low-risk group in the three independent cohorts, and the time-dependent ROC curve showed that the five-gene (STMN1, PON1, PLOD2, MARCKSL1, and SPP1) risk score with a high accuracy in predicting OS. The patients with AFP &gt;300 ng/ml, tumor poor differentiation (grade 3–4), micro and macro vascular tumor invasion, advanced stage (AJCC III-IV, BCLC stage B-C, and CLIP score &gt;2) exhibited a higher risk score. Subgroup survival analysis found that the risk score was applicable to patients with different clinical characteristics. GO and KEGG functional enrichment analysis revealed that cell cycle, p53 signaling, TNF signaling-related pathways were upregulated in the high-risk group. The higher infiltration level of activated CD4 T cell, CD56 bright natural killer cell, plasmacytoid dendritic cell, and type 2 T helper cells were found to lead an unfavorable impact on the OS of HCC patients, and these four kinds of immune cells exhibited a higher infiltration level in the high-risk group.

Conclusion: The five-gene risk score proposed in the research may provide new insights into the individualized evaluation of HCC prognosis.

A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma

Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis.

Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups.

Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score.

Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

Ferroptosis: A Double-Edged Sword in Gastrointestinal Disease

Ferroptosis is a novel form of regulated cell death (RCD) that is typically accompanied by iron accumulation and lipid peroxidation. In contrast to apoptosis, autophagy, and necroptosis, ferroptosis has unique biological processes and pathophysiological characteristics. Since it was first proposed in 2012, ferroptosis has attracted attention worldwide. Ferroptosis is involved in the progression of multiple diseases and could be a novel therapeutic target in the future. Recently, tremendous progress has been made regarding ferroptosis and gastrointestinal diseases, including intestinal ischemia/reperfusion (I/R) injury, inflammatory bowel disease (IBD), gastric cancer (GC), and colorectal cancer (CRC). In this review, we summarize the recent progress on ferroptosis and its interaction with gastrointestinal diseases. Understanding the role of ferroptosis in gastrointestinal disease pathogenesis could provide novel therapeutic targets for clinical treatment.

Identification and validation of a novel immune-related signature associated with macrophages and CD8 T cell infiltration predicting overall survival for hepatocellular carcinoma

BACKGROUND

Although the effects of macrophages and CD8 T cell infiltration on clinical outcome have been widely reported, the association between immunity-associated gene with them for hepatocellular carcinoma (HCC) remains unclear.

MATERIALS AND METHODS

The ssGSEA served for quantifying the macrophages as well as CD8 T cell infiltration in the HCC samples obtained from TCGA database. Kaplan-Meier (KM) survival assay was used to determine the associations between macrophages and CD8 T cell infiltration with OS. LASSO Cox regressive method assisted in developing an immune gene signature as well as building a risk score. The performance was evaluated by the time-dependent ROC together with the KM survival analysis. The ICGC database were adopted for external verification. CIBERSORT was applied to the correlation analysis on the immune-related signature and the immunocyte infiltration. GSEA were employed exploring the underlying molecular mechanisms.

RESULTS

Increased CD8+ T cell infiltration was associated with longer OS, whereas a greater infiltration of macrophages was related to shorter OS. There were 398 differential expression genes (DEGs) between the high- and low infiltration groups with the "edgeR" package. An prognostic signature consisted of 10 immune genes was built in TCGA and examined in ICGC. The uniform cutoff (0.927) was adopted for separating sufferers into the high-risk (HR) and low-risk (LR) groups. The ROC curves revealed that the AUC data for this signature predicting 1, 2, 3, 4 and 5 year were all above 0.7 in both TCGA and ICGC cohort and patients in the HR group exhibited an evidently weaker prognostic results compared with the LR group. The HR group presented evidently greater Tregs and Macrophage M0 relative to the LR group, whereas the LR group saw the enrichment of CD8 T cells.

CONCLUSION

The immune signature associated with macrophages as well as CD8 T cell infiltration has reliable prognostic and predictive value for HCC patients.