Identification of a novel immune prognostic model in gastric cancer

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND

Gastric cancer (GC) is a common malignancy of the digestive system. According to global 2018 cancer data, GC has the fifth-highest incidence and the third-highest fatality rate among malignant tumors. More than 60% of GC are linked to infection with Helicobacter pylori (H. pylori), a gram-negative, active, microaerophilic, and helical bacterium. This parasite induces GC by producing toxic factors, such as cytotoxin-related gene A, vacuolar cytotoxin A, and outer membrane proteins. Ferroptosis, or iron-dependent programmed cell death, has been linked to GC, although there has been little research on the link between H. pylori infection-related GC and ferroptosis.

AIM

To identify coregulated differentially expressed genes among ferroptosis-related genes (FRGs) in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.

METHODS

Gene expression profiles of GC patients and those with H. pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The FRGs were acquired from the FerrDb database. A ferroptosis-related gene prognostic index (FRGPI) was created using least absolute shrinkage and selection operator-Cox regression. The predictive ability of the FRGPI was validated in the GEO cohort. Finally, we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.

RESULTS

Four hub genes were identified (NOX4, MTCH1, GABARAPL2, and SLC2A3) and shown to accurately predict GC and H. pylori-associated GC. The FRGPI based on the hub genes could independently predict GC patient survival; GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group. The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression. Moreover, the gene expression levels of common immune checkpoint proteins dramatically increased in the high-risk subgroup of the FRGPI cohort. The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane. The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.

CONCLUSION

In this study, we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent cancers and accounts for a significant proportion of cancer-associated deaths worldwide. This disease, marked by multifaceted etiology, often poses diagnostic challenges. Finding a reliable and non-invasive diagnostic method seems to be necessary. In this study, we analyzed the gene expression profiles of 20 HCC patients, 12 individuals with chronic hepatitis, and 15 healthy controls. Enrichment analysis revealed that platelet aggregation, secretory granule lumen, and G-protein-coupled purinergic nucleotide receptor activity were common biological processes, cellular components, and molecular function in HCC and chronic hepatitis B (CHB) compared to healthy controls, respectively. Furthermore, pathway analysis demonstrated that "estrogen response" was involved in the pathogenesis of HCC and CHB conditions, while, "apoptosis" and "coagulation" pathways were specific for HCC. Employing computational feature selection and logistic regression classification, we identified candidate genes pivotal for diagnostic panel development and evaluated the performance of these panels. Subsequent machine learning evaluations assessed these panels' performance in an independent cohort. Remarkably, a 3-marker panel, comprising RANSE2, TNF-α, and MAP3K7, demonstrated the best performance in qRT-PCR-validated experimental data, achieving 98.4% accuracy and an area under the curve of 1. Our findings highlight this panel's promising potential as a non-invasive approach not only for detecting HCC but also for distinguishing HCC from CHB patients.

Involvement of CXC chemokines (CXCL1-CXCL17) in gastric cancer: Prognosis and therapeutic molecules

Gastric cancer (GC) is the fifth-most prevalent and second-most deadly cancer worldwide. Due to the late onset of symptoms, GC is frequently treated at a mature stage. In order to improve the diagnostic and clinical decision-making processes, it is necessary to establish more specific and sensitive indicators valuable in the early detection of the disease whenever a cancer is asymptomatic. In this work, we gathered information about CXC chemokines and GC by using scientific search engines including Google Scholar, PubMed, SciFinder, and Web of Science. Researchers believe that GC chemokines, small proteins, class CXC chemokines, and chemokine receptors promote GC inflammation, initiation, and progression by facilitating angiogenesis, tumor transformation, invasion, survival, metastatic spread, host response safeguards, and inter-cell interaction. With our absolute best professionalism, the role of CXC chemokines and their respective receptors in GC diagnosis and prognosis has not been fully explained. This review article updates the general characteristics of CXC chemokines, their unique receptors, their function in the pathological process of GC, and their potential application as possible indicators for GC. Although there have only recently been a few studies focusing on the therapeutic efficacy of CXC chemokine inhibitors in GC, growing experimental evidence points to the inhibition of CXC chemokines as a promising targeted therapy. Therefore, further translational studies are warranted to determine whether specific antagonists or antibodies designed to target CXC chemokines alone or in combination with chemotherapy are useful for diagnosing advanced GC.

L3MBTL3 Is a Potential Prognostic Biomarker and Correlates with Immune Infiltrations in Gastric Cancer

Recent research has linked lethal (3) malignant brain tumor-like 3 (L3MBTL3) to cancer aggressiveness and a dismal prognosis, but its function in gastric cancer (GC) is unclear. This research investigated the association between L3MBTL3 expression and clinicopathological characteristics of GC cases, as well as its prognostic value and biological function based on large-scale databases and clinical samples. The results showed that L3MBTL3 expression was upregulated in malignant GC tissues, which was associated with a shortened survival time and poor clinicopathological characteristics, including TNM staging. A functional enrichment analysis including GO/KEGG and GSEA illustrated the enrichment of different L3MBTL3-associated pathways involved in carcinogenesis and immune response. In addition, the correlations between L3MBTL3 and tumor-infiltrating immune cells were determined based on the TIMER database; the results showed that L3MBTL3 was associated with the immune infiltration of macrophages and their polarization from M1 to M2. Furthermore, our findings suggested a possible function for L3MBTL3 in the regulation of the tumor immune microenvironment of GC. In summary, L3MBTL3 has diagnostic potential, and it also offers new insights into the development of aggressiveness and prognosis in GC.

Co-expression pattern of SLC transporter genes associated with the immune landscape and clinical outcomes in gastric cancer

Solute carrier (SLC) transporters play a dual role in the occurrence and progression of tumours by acting as both suppressors and promoters. However, the overall impact of SLC transcriptome signatures on the tumour microenvironment, biological behaviour and clinical stratification of gastric cancer has not been thoroughly investigated. Therefore, we comprehensively analysed the expression profiles of the SLC transporter family members to identify novel molecular subtypes in gastric cancer. We identified two distinct SLC subtypes, SLC-S1 and SLC-S2, using non-negative matrix factorization. These subtypes were markedly linked with the tumour microenvironment landscape, biological pathway activation and distinct clinical features of gastric cancer. Furthermore, a new scoring model, the SLC score, was developed to quantify the SLC subtypes. High SLC scores indicated a pattern of 'SLC-S2', characterized by stromal infiltration and activation, poor prognosis and insensitivity to chemotherapy and immunotherapy, but high sensitivity to imatinib. The SLC score could serve as a supplement to the Tumour Node Metastasis (TNM) staging system to guide personalized treatment strategies and predict prognosis for patients with gastric cancer.

PRKAA1 predicts prognosis and is associated with immune characteristics in gastric cancer

PRKAA1 is the α-subunit of 5-AMP-activated protein kinase. This study aimed to investigate the role of PRKAA1 expression with multiple clinical parameters, the overall survival rate, blood indexes, and immune infiltration in gastric cancer (GC) patients. We investigated PRKAA1 expression data in GC patients using ELISA, protein atlas, UALCAN, and GEPIA. PRKAA1 expression was associated with immune cell infiltration, and immune cell types were analyzed with the TIMER, DICE, and protein atlas databases. We compared the level of PRKAA1 expression based on the clinical features of GC patients (n = 345). GC patients were divided into two groups based on PRKAA1 expression, and the lymphocyte subsets, overall survival rate, and clinical parameters were compared with peripheral blood mononuclear cell and biochemical indexes. PRKAA1 was highly expressed in the serum of GC patients compared with that of healthy individuals. GC patients with distant metastases, a later TNM stage, and stage IV in UICC exhibited higher PRKAA1 expression. PRKAA1 expression was significantly correlated with circulating T cells. The protein atlas and DICE database results confirmed that PRKAA1 was closely associated with T cells in a single-cell cluster. Furthermore, GC patients with low PRKAA1 expression had better OS rates. PRKAA1 may serve as a potential prognostic biomarker for GC and have an association with immune infiltrates.

Identification of diagnostic biomarkers for idiopathic pulmonary hypertension with metabolic syndrome by bioinformatics and machine learning

Idiopathic pulmonary hypertension (IPAH) is a condition that affects various tissues and organs and the metabolic and inflammatory systems. The most prevalent metabolic condition is metabolic syndrome (MS), which involves insulin resistance, dyslipidemia, and obesity. There may be a connection between IPAH and MS, based on a plethora of studies, although the underlying pathogenesis remains unclear. Through various bioinformatics analyses and machine learning algorithms, we identified 11 immune- and metabolism-related potential diagnostic genes (EVI5L, RNASE2, PARP10, TMEM131, TNFRSF1B, BSDC1, ACOT2, SAC3D1, SLA2, P4HB, and PHF1) for the diagnosis of IPAH and MS, and we herein supply a nomogram for the diagnosis of IPAH in MS patients. Additionally, we discovered IPAH's aberrant immune cells and discuss them here.

Development and validation of a necroptosis-related gene prognostic score to predict prognosis and efficiency of immunotherapy in gastric cancer

Necroptosis is a novel type of regulated cell death that is intimately associated with a variety of tumors. However, how necroptosis affects the identification of gastric cancer (GC) remains unclear. Here we seek to find new potential necroptosis-related biomarkers to predict GC prognosis and immunotherapy effect. We used Cox analysis to obtain shared prognostic markers related to necroptosis from five datasets (TCGA and four GEO datasets). Then, a necroptosis-related gene prognostic score (NRGPS) system was constructed using LASSO Cox regression, NRGPS consisting of three necroptosis-related mRNAs (AXL, RAI14, and NOX4) was identified, 31 pairs of GC and adjacent normal tissues from the Second Hospital of Harbin Medical University were collected and Real-Time Quantitative PCR (RT-qPCR) was used to detect the relative expression levels of the three necroptosis-related mRNAs, and external validation was performed on four GEO datasets (GSE84437, GSE26901, GSE62254 and GSE15459). In this study, Overall survival (OS) in the high-NRGPS group was significantly lower than in the low-NRGPS group. Cox regression analyses showed that NRGPS was an independent prognostic variable. Tumor-mutation-burden (TMB), tumor microenvironment (TME), microsatellite instability (MSI), and Tumor Immune Dysfunction and Exclusion (TIDE) scoring were used as predictors of the immunotherapy response. A cancer-friendly immune microenvironment, a high TIDE score, a low TMB, and a low MSI were all characteristics of the high-NRGPS group, and they all consistently showed that the issues seen there are related to immune escape in GC. The combination of three candidate genes may be an effective method for diagnostic assessment of GC prognosis and immunotherapy efficacy.

Ribonuclease A Family Member 2 Promotes the Malignant Progression of Glioma Through the PI3K/Akt Signaling Pathway

The treatment of patients with glioma still faces many difficulties. To further optimize treatment, it is necessary to identify more accurate markers as treatment targets and predict prognostic indicators. RNASE2 was identified as a differentially expressed gene (DEG) in glioma tissues using bioinformatics analysis. In glioma microarrays, 31.21% (54/173) and 68.79% (119/173) patients showed low and high RNASE2 protein expression levels, respectively. RNASE2 protein levels were considerably correlated with age, WHO grade, relapse, and death. Both mRNA and protein levels were associated with the overall survival of patients with glioma. To investigate the role of RNASE2, it was overexpressed or silenced in glioma cells. RNASE2 overexpression promoted cell proliferation, migration, and invasion. In addition, its overexpression promoted the growth of subcutaneous tumors and lung metastasis of glioma cells. Key protein levels in the PI3K/Akt signaling pathway were upregulated by RNASE2 overexpression. In contrast, RNASE2 knockdown had the opposite effects. Furthermore, LY294002 blocked the effects of RNASE2 on the cell function of glioma cells. In conclusion, RNASE2 is a novel marker associated with the diagnosis and prognosis of patients with glioma, and it promotes the malignant progression of gliomas through the PI3K/Akt signaling pathway.

A Risk Model of Eight Immune-Related Genes Predicting Prognostic Response to Immune Therapies for Gastric Cancer

Immune checkpoint inhibitor (ICI) treatment is considered as an innovative approach for cancers. Since not every patient responded well to ICI therapy, it is imperative to screen out novel signatures to predict prognosis. Based on 407 gastric cancer (GC) samples retrieved from The Cancer Genome Atlas (TCGA), 36 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA), and eight of them (RNASE2, CGB5, INHBE, DUSP1, APOA1, CD36, PTGER3, CTLA4) were used to formulate the Cox regression model. The obtained risk score was proven to be significantly correlated with overall survival (OS), consistent with the consequence of the Gene Expression Omnibus (GEO) cohort (n = 433). Then, the relationship between the risk score and clinical, molecular and immune characteristics was further investigated. Results showed that the low-risk subgroup exhibited higher mutation rate, more M1 macrophages, CD8+ and CD4+ T cells infiltrating, more active MHC-I, and bias to “IFN-γ Dominant” immune type, which is consistent with our current understanding of tumor prognostic risk. Furthermore, it is suggested that our model can accurately predict 1-, 2-, and 3-year OS of GC patients, and that it was superior to other canonical models, such as TIDE and TIS. Thus, these eight genes are probably considered as potential signatures to predict prognosis and to distinguish patient benefit from ICI, serving as a guiding individualized immunotherapy.

Development and Verification of an Immune-Based Gene Signature for Risk Stratification and Immunotherapeutic Efficacy Assessment in Gastric Cancer

Objective

Due to the molecular heterogeneity of gastric cancer, only minor patients respond to immunotherapeutic schemes. This study is aimed at developing an immune-based gene signature for risk stratification and immunotherapeutic efficacy assessment in gastric cancer.

Methods

An immune-based gene signature was developed in gastric cancer by LASSO method in the training set. The predictive performance was validated in the external datasets. KEGG pathways related to risk scores were assessed by GSEA. Based on multivariate Cox regression analysis, a nomogram was established. Sensitivity to chemotherapy drugs was evaluated between high- and low-risk samples. The relationships of risk scores with infiltration levels of immune cells, stromal scores, immune scores, immune cell subgroups, and overall response to anti-PD-L1 therapy were determined.

Results

Our results showed that high risk scores were indicative of undesirable survival outcomes both in the training set (p < 0.0001) and the validation set (p = 0.002). Moreover, this signature could independently predict patients' survival (HR: 2.656 (1.919-3.676) and p < 0.001). Subgroup analysis confirmed the sensitivity of this signature in predicting prognosis (all p < 0.05). Cancer-related pathways were primarily enriched in high-risk samples, such as MAPK and TGF-β pathways (p < 0.05). By incorporating stage and the risk score, we established a nomogram for predicting one-, three-, and five-year survival probability. Patients with high-risk scores were more sensitive to chemotherapy drugs (p < 0.05). There was heterogeneity in immune cells between high- and low-risk samples (p < 0.05). Samples with progressive disease exhibited the highest risk score, and those with complete response had the lowest risk score (p < 0.05).

Conclusion

This immune-based gene signature might be representative of a promising prognostic classifier for predicting risk stratification and immunotherapeutic efficacy in gastric cancer, assisting personalized therapy and follow-up plan.