Prognostic Ferroptosis-Related lncRNA Signatures Associated With Immunotherapy and Chemotherapy Responses in Patients With Stomach Cancer

Hub genes, a diagnostic model, and immune infiltration based on ferroptosis-linked genes in schizophrenia

Background

Schizophrenia (SCZ) is a prevalent and serious mental disorder, and the exact pathophysiology of this condition is not fully understood. In previous studies, it has been proven that ferroprotein levels are high in SCZ. It has also been shown that this inflammatory response may modify fibromodulin. Accumulating evidence indicates a strong link between metabolism and ferroptosis. Therefore, the present study aims to identify ferroptosis-linked hub genes to further investigate the role that ferroptosis plays in the development of SCZ.

Material and methods

From the GEO database, four microarray data sets on SCZ (GSE53987, GSE38481, GSE18312, and GSE38484) and ferroptosis-linked genes were extracted. Using the prefrontal cortex expression matrix of SCZ patients and healthy individuals as the control group from GSE53987, weighted gene co-expression network analysis (WGCNA) was performed to discover SCZ-linked module genes. From the feed, genes associated with ferroptosis were retrieved. The intersection of the module and ferroptosis-linked genes was done to obtain the hub genes. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were conducted. The SCZ diagnostic model was established using logistic regression, and the GSE38481, GSE18312, and GSE38484 data sets were used to validate the model. Finally, hub genes linked to immune infiltration were examined.

Results

A total of 13 SCZ module genes and 7 hub genes linked to ferroptosis were obtained: DECR1, GJA1, EFN2L2, PSAT1, SLC7A11, SOX2, and YAP1. The GO/KEGG/GSEA study indicated that these hub genes were predominantly enriched in mitochondria and lipid metabolism, oxidative stress, immunological inflammation, ferroptosis, Hippo signaling pathway, AMP-activated protein kinase pathway, and other associated biological processes. The diagnostic model created using these hub genes was further confirmed using the data sets of three blood samples from patients with SCZ. The immune infiltration data showed that immune cell dysfunction enhanced ferroptosis and triggered SCZ.

Conclusion

In this study, seven critical genes that are strongly associated with ferroptosis in patients with SCZ were discovered, a valid clinical diagnostic model was built, and a novel therapeutic target for the treatment of SCZ was identified by the investigation of immune infiltration.

Exploring hub genes and crucial pathways linked to oxidative stress in bipolar disorder depressive episodes through bioinformatics analysis

Background

Bipolar disorder (BD) is a complex and serious psychiatric condition primarily characterized by bipolar depression, with the underlying genetic determinants yet to be elucidated. There is a substantial body of literature linking psychiatric disorders, including BD, to oxidative stress (OS). Consequently, this study aims to assess the relationship between BD and OS by identifying key hub genes implicated in OS pathways.

Methods

We acquired gene microarray data from GSE5392 through the Gene Expression Omnibus (GEO). Our approach encompassed differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction (PPI) Network analysis to pinpoint hub genes associated with BD. Subsequently, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to identify hub genes relevant to OS. To evaluate the diagnostic accuracy of these hub genes, we performed receiver operating characteristic curve (ROC) analysis on both GSE5388 and GSE5389 datasets. Furthermore, we conducted a study involving ten BD patients and ten healthy controls (HCs) who met the special criteria, assessing the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs).

Results

We identified 411 down-regulated genes and 69 up-regulated genes for further scrutiny. Through WGCNA, we obtained 22 co-expression modules, with the sienna3 module displaying the strongest association with BD. By integrating differential analysis with genes linked to OS, we identified 44 common genes. Subsequent PPI Network and WGCNA analyses confirmed three hub genes as potential biomarkers for BD. Functional enrichment pathway analysis revealed their involvement in neuronal signal transduction, oxidative phosphorylation, and metabolic obstacle pathways. Using the Cytoscape plugin "ClueGo assay," we determined that a majority of these targets regulate neuronal synaptic plasticity. ROC curve analysis underscored the excellent diagnostic value of these three hub genes. Quantitative reverse transcription-PCR (RT-qPCR) results indicated significant changes in the expression of these hub genes in the PBMCs of BD patients compared to HCs.

Conclusion

We identified three hub genes (TAC1, MAP2K1, and MAP2K4) in BD associated with OS, potentially influencing the diagnosis and treatment of BD. Based on the GEO database, our study provides novel insights into the relationship between BD and OS, offering promising therapeutic targets.

Identification and Functional Characterization of PI3K/Akt/mTOR Pathway-Related lncRNAs in Lung Adenocarcinoma: A Retrospective Study

OBJECTIVE

This paper aimed to investigate the PI3K/Akt/mTOR signal-pathway regulator factor-related lncRNA signatures (PAM-SRFLncSigs), associated with regulators of the indicated signaling pathway in patients with lung adenocarcinoma (LUAD) undergoing immunotherapy.

MATERIALS AND METHODS

In this retrospective study, we employed univariate Cox, multivariate Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses to identify prognostically relevant long non-coding RNAs (lncRNAs), construct prognostic models, and perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Subsequently, immunoassay and chemotherapy drug screening were conducted. Finally, the prognostic model was validated using the Imvigor210 cohort, and tumor stem cells were analyzed.

RESULTS

We identified seven prognosis-related lncRNAs (AC084757.3, AC010999.2, LINC02802, AC026979.2, AC024896.1, LINC00941 and LINC01312). We also developed prognostic models to predict survival in patients with LUAD. KEGG enrichment analysis confirmed association of LUAD with the PI3K/Akt/mTOR signaling pathway. In the analysis of immune function pathways, we discovered three good prognostic pathways (Cytolytic_activity, Inflammation-promoting, T_cell_co-inhibition) in LUAD. Additionally, we screened 73 oncology chemotherapy drugs using the "pRRophetic" algorithm.

CONCLUSION

Identification of seven lncRNAs linked to regulators of the PI3K/Akt/mTOR signaling pathway provided valuable insights into predicting the prognosis of LUAD, understanding the immune microenvironment and optimizing immunotherapy strategies.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

Purpose

Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value.

Patients and Methods

In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune microenvironment analysis, mutation analysis, and column line graph generation.

Results

We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load.

Conclusion

We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.

Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder prevalent among older adults. Although AD symptoms can be managed through certain treatments, advancing the understanding of underlying disease mechanisms and developing effective therapies is critical.

Methods

In this study, we systematically analyzed transcriptome data from temporal lobes of healthy individuals and patients with AD to investigate the relationship between AD and mitochondrial autophagy. Machine learning algorithms were used to identify six genes-FUNDC1, MAP1LC3A, CSNK2A1, VDAC1, CSNK2B, and ATG5-for the construction of an AD prediction model. Furthermore, AD was categorized into three subtypes through consensus clustering analysis.

Results

The identified genes are closely linked to the onset and progression of AD and can serve as reliable biomarkers. The differences in gene expression, clinical features, immune infiltration, and pathway enrichment were examined among the three AD subtypes. Potential drugs for the treatment of each subtype were also identified.

Discussion

The findings observed in the present study can help to deepen the understanding of the underlying disease mechanisms of AD and enable the development of precision medicine and personalized treatment approaches.

Inferring drug-disease associations by a deep analysis on drug and disease networks

Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.

Long non-coding RNAs and gastric cancer: An update of potential biomarkers and therapeutic applications

The frequent metastasis of gastric cancer (GC) complicates the cure and therefore the development of effective diagnostic and therapeutic approaches is urgently necessary. In recent years, lncRNA has emerged as a drug target in the treatment of GC, particularly in the areas of cancer immunity, cancer metabolism, and cancer metastasis. This has led to the demonstration of the importance of these RNAs as prognostic, diagnostic and therapeutic agents. In this review, we provide an overview of the biological activities of lncRNAs in GC development and update the latest pathological activities, prognostic and diagnostic strategies, and therapeutic options for GC-related lncRNAs.

Immune infiltration and diagnostic value of immune-related genes in periodontitis using bioinformatics analysis

BACKGROUND AND OBJECTIVES

Periodontitis, which is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue, is the leading cause of tooth loss in adults. Many studies have found that inflammatory immune responses are involved in the risk of periodontal tissue damage. Therefore, we analyzed the association between immunity and periodontitis using bioinformatics methods to further understand this disease.

MATERIALS AND METHODS

First, the expression profiles of periodontitis and healthy samples were downloaded from the GEO database, including a training dataset GSE16134 and an external validation dataset GSE10334. Then, differentially expressed genes were identified using the limma package. Subsequently, immune cell infiltration was calculated by using the CIBERSORT algorithm. We further identified genes linking periodontitis and immunity from the ImmPort and DisGeNet databases. In addition, some of them were selected to construct a diagnostic model via a logistic stepwise regression analysis.

RESULTS AND CONCLUSIONS

Two hundred sixty differentially expressed genes were identified and found to be involved in responses to bacterial and immune-related processes. Subsequently, immune cell infiltration analysis demonstrates significant differences in the abundance of most immune cells between periodontitis and healthy samples, especially in plasma cells. These results suggested that immunity doses play a non-negligible role in periodontitis. Twenty-one genes linking periodontitis and immunity were further identified. And nine hub genes of them were identified that may be key genes involved in the development of periodontitis. Gene ontology analyses showed that these genes are involved in response to molecules of bacterial origin, cell chemotaxis, and response to chemokines. In addition, three genes of them were selected to construct a diagnostic model. And its good diagnostic performance was demonstrated by the receiver operating characteristic curves, with an area under the curve of 0.9424 for the training dataset and 0.9244 for the external validation dataset.

Metabolism-related long non-coding RNA in the stomach cancer associated with 11 AMMLs predictive nomograms for OS in STAD

Background: The metabolic processes involving amino acids are intimately linked to the onset and progression of cancer. Long non-coding RNAs (LncRNAs) perform an indispensable function in the modulation of metabolic processes as well as the advancement of tumors. Non-etheless, research into the role that amino acid metabolism-related LncRNAs (AMMLs) might play in predicting the prognosis of stomach adenocarcinoma (STAD) has not been done. Therefore, This study sought to design a model for AMMLs to predict STAD-related prognosis and elucidate their immune properties and molecular mechanisms.

Methods: The STAD RNA-seq data in the TCGA-STAD dataset were randomized into the training and validation groups in a 1:1 ratio, and models were constructed and validated respectively. In the molecular signature database, This study screened for genes involved in amino acid metabolism. AMMLs were obtained by Pearson’s correlation analysis, and predictive risk characteristics were established using least absolute shrinkage and selection operator (LASSO) regression, univariate Cox analysis, and multivariate Cox analysis. Subsequently, the immune and molecular profiles of high- and low-risk patients and the benefit of the drug were examined.

Results: Eleven AMMLs (LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, LINCOG, LINC00449, LINC01819, and UBE2R2-AS1) were used to develop a prognostic model. Moreover, high-risk individuals had worse overall survival (OS) than low-risk patients in the validation and comprehensive groups. A high-risk score was associated with cancer metastasis as well as angiogenic pathways and high infiltration of tumor-associated fibroblasts, Treg cells, and M2 macrophages; suppressed immune responses; and a more aggressive phenotype.

Conclusion: This study identified a risk signal associated with 11 AMMLs and established predictive nomograms for OS in STAD. These findings will help us personalize treatment for gastric cancer patients.

Effects of spaceflight on the spleen and thymus of mice: Gene pathway analysis and immune infiltration analysis

During space flight, the immune system function of the body is disrupted due to continuous weightlessness, radiation and other factors, resulting in an increased incidence of infectious diseases in astronauts. However, the effect of space flight on the immune system at the molecular level is unknown. The aim of this study was to identify key genes and pathways of spatial environmental effects on the spleen and thymus using bioinformatics analysis of the GEO dataset. Differentially expressed genes (DEGs) in the spleen and thymus of mice preflight and postflight were screened by comprehensive analysis of gene expression profile data. Then, GO enrichment analysis of DEGs was performed to determine the biological role of DEGs. A protein-protein interaction network was used to identify hub genes. In addition, transcription factors in DEGs were screened, and a TF-target regulatory network was constructed. Finally, immune infiltration analysis was performed on spleen and thymus samples from mice. The results showed that DEGs in the spleen and thymus are mainly involved in immune responses and in biological processes related to platelets. Six hub genes were identified in the spleen and 13 in the thymus, of which Ttr, Aldob, Gc and Fabp1 were common to both tissues. In addition, 5 transcription factors were present in the DEGs of the spleen, and 9 transcription factors were present in the DEGs of the thymus. The spatial environment can influence the degree of immune cell infiltration in the spleen and thymus. Our study bioinformatically analyzed the GEO dataset of spacefaring mice to identify the effects of the space environment on the immune system and the genes that play key roles, providing insights for the treatment of spaceflight-induced immune system disorders.

The effects of esketamine on the intestinal microenvironment and intestinal microbiota in mice

OBJECTIVE

This study aimed to investigate the impact of esketamine on the intestinal flora and microenvironment in mice using mRNA transcriptome sequencing and 16S rRNA sequencing.

METHODS

Ten female mice were randomly assigned to two groups. One group received daily intramuscular injections of sterile water, while the other group received esketamine. After 24 days, the mice were sacrificed, and their intestinal tissues and contents were collected for 16S rRNA sequencing and mRNA transcriptome sequencing. The intergroup differences in the mouse intestinal flora were analyzed. Differentially expressed genes were utilized to construct ceRNA networks and transcription factor regulatory networks to assess the effects of esketamine on the intestinal flora and intestinal tissue genes.

RESULTS

Esketamine significantly altered the abundance of intestinal microbiota, including Adlercreutzia equolifaciens and Akkermansia muciniphila. Differential expression analysis revealed 301 significantly upregulated genes and 106 significantly downregulated genes. The ceRNA regulatory network consisted of 6 lncRNAs, 44 miRNAs, and 113 mRNAs, while the regulatory factor network included 13 transcription factors and 53 target genes. Gene Ontology enrichment analysis indicated that the differentially expressed genes were primarily associated with immunity, including B-cell activation and humoral immune response mediation. The biological processes in the ceRNA regulatory network primarily involved transport, such as organic anion transport and monocarboxylic acid transport. The functional annotation of target genes in the TF network was mainly related to epithelial cells, including epithelial cell proliferation and regulation.

CONCLUSION

Esketamine induces changes in gut microbiota and the intestinal microenvironment, impacting the immune environment and transport modes.

A prognostic and therapeutic hallmark developed by the integrated profile of basement membrane and immune infiltrative landscape in lung adenocarcinoma

Basement membranes (BMs) are specialised extracellular matrices that maintain cellular integrity and resist the breaching of carcinoma cells for metastases while regulating tumour immunity. The tumour immune microenvironment (TME) is essential for tumour growth and the response to and benefits from immunotherapy. In this study, the BM score and TME score were constructed based on the expression signatures of BM-related genes and the presence of immune cells in lung adenocarcinoma (LUAD), respectively. Subsequently, the BM-TME classifier was developed with the combination of BM score and TME score for accurate prognostic prediction. Further, Kaplan-Meier survival estimation, univariate Cox regression analysis and receiver operating characteristic curves were used to cross-validate and elucidate the prognostic prediction value of the BM-TME classifier in several cohorts. Findings from functional annotation analysis suggested that the potential molecular regulatory mechanisms of the BM-TME classifier were closely related to the cell cycle, mitosis and DNA replication pathways. Additionally, the guiding value of the treatment strategy of the BM-TME classifier for LUAD was determined. Future clinical disease management may benefit from the findings of our research.

A novel risk model construction and immune landscape analysis of gastric cancer based on cuproptosis-related long noncoding RNAs

Recent studies have identified cuproptosis, a new mechanism of regulating cell death. Accumulating evidence suggests that copper homeostasis is associated with tumorigenesis and tumor progression, however, the clinical significance of cuproptosis in gastric cancer (GC) is unclear. In this study, we obtained 26 prognostic cuproptosis-related lncRNAs (CRLs) based on 19 cuproptosis-related genes (CRGs) via Pearson correlation analysis, differential expression analysis, and univariate Cox analysis. A risk model based on 10 CRLs was established with the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazards model to predict the prognosis and immune landscape of GC patients from The Cancer Genome Atlas (TCGA). The risk model has excellent accuracy and efficiency in predicting prognosis of GC patients (Area Under Curve (AUC) = 0.742, 0.803, 0.806 at 1,3,5 years, respectively, P < 0.05). In addition, we found that the risk score was negatively correlated with the infiltration of natural killer (NK) cells and helper T cells, while positively correlated with the infiltration of monocytes, macrophages, mast cells, and neutrophils. Moreover, we evaluated the difference in drug sensitivity of patients with different risk patterns. Furthermore, low-risk patients showed higher tumor mutation burden (TMB) and better immunotherapy response than high-risk patients. In the end, we confirmed the oncogenic role of AL121748.1 which exhibited the highest Hazard Ratio (HR) value among 10 CRLs in GC via cellular functional experiments. In conclusion, our risk model shows a significant role in tumor immunity and could be applied to predict the prognosis of GC patients.

Effects of the Targeted Regulation of CCRK by miR-335-5p on the Proliferation and Tumorigenicity of Human Renal Carcinoma Cells

Cell cycle-related kinase (CCRK) is most closely related to cyclin-dependent protein kinase, which may activate cyclin-dependent kinase 2 and is associated with the growth of human cancer cells. However, the expression and function of CCRK in the pathogenesis of clear cell renal cell cancer (ccRCC) are unclear. Herein, this research aimed to explore the potential mechanism of the targeted regulation of CCRK by miR-335-5p on the proliferation and tumorigenicity of human ccRCC cells. The results showed that CCRK was significantly overexpressed in ccRCC tissues and cells, and knockdown of the CCRK expression by shRNA inhibited cell proliferation in vitro and in vivo and enhanced cell apoptosis in vitro, which indicated that CCRK could be a potential target for antitumour drugs in the treatment of ccRCC. Moreover, miR-335-5p was found to bind directly to the 3′ untranslated region of CCRK, was expressed at markedly low levels in ccRCC cells, and was closely associated with the tumour stage. The overexpression of CCRK partially reversed the inhibitory effects of miR-335-5p on the cell growth of ccRCC, which implied that miR-335-5p could serve as a promising tumour inhibitor for ccRCC. In summary, CCRK could serve as an alternative antitumour drug target, and miR-335-5p could be a promising therapeutic tumour inhibitor for ccRCC treatment.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.

Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.

Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.

Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

The Long Noncoding RNA MEG3 Retains Epithelial-Mesenchymal Transition by Sponging miR-146b-5p to Regulate SLFN5 Expression in Breast Cancer Cells

More and more studies have shown that long noncoding RNAs (lncRNAs) play essential roles in malignant tumors. The lncRNA MEG3 serves as a crucial molecule in breast cancer development, but the specific molecular mechanism needs to be further explored. We previously reported that Schlafen family member 5 (SLFN5) inhibits breast cancer malignant development by regulating epithelial-mesenchymal transition (EMT), invasion, and proliferation/apoptosis. Herein, we demonstrated that MEG3 was downregulated in pan-cancers and correlated with SLFN5 expression positively in breast cancer by bioinformatics analysis of TCGA and UCSC Xena data. Intervention with MEG3 positively affected SLFN5 expression in breast cancer cells. MEG3 repressed EMT and migration/invasion, similar to our previously reported functions of SLFN5 in breast cancer. Through bioinformatics analysis of starBase and LncBase data, 12 miRNAs were found to regulate both SLFN5 and MEG3, in which miR-146b-5p was confirmed to be regulated by MEG3 using MEG3 siRNA and overexpression method. MiR-146b-5p could bind to both SLFN5 3′UTR and MEG3, and inhibit their expression in a competing endogenous RNA mechanism, assayed by luciferase reporter and RNA pull down methods. Therefore, we conclude that MEG3 positively modulates SLFN5 expression by sponging miR-146b-5p and inhibits breast cancer development.

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.

Immunological and prognostic analysis of PSENEN in low-grade gliomas: An immune infiltration-related prognostic biomarker

Metformin is widely used in the treatment of type 2 diabetes (T2D) and plays a role in antitumor and antiobesity processes. A recent study identified its direct molecular target, PEN2 (PSENEN). PSENEN is the minimal subunit of the multiprotein complex γ-secretase, which promotes the differentiation of oligodendrocyte progenitors into astrocytes in the central nervous system. This study was mainly based on gene expression data and clinical data from the TCGA and CGGA databases. Analysis of differential expression of PSENEN between tissues from 31 cancers and paracancerous tissues revealed that it had high expression levels in most cancers except 2 cancers. Using univariate Cox regression analysis and Kaplan-Meier survival analysis, a high expression level of PSENEN was shown to be a risk factor in low-grade gliomas (LGG). Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses indicated that PSENEN is widely involved in immune-related signaling pathways in LGG. PSENEN expression level was significantly associated with TMB, MSI, tumor stemness index, and the expression levels of immunomodulatory genes in LGG. Finally, immune infiltration analysis revealed that PSENEN level was associated with the presence of various immune infiltrating cells, among which PSENEN was strongly associated with the presence of M2 macrophages and played a synergistic pro-cancer role. In conclusion, PSENEN may partially influence prognosis by modulating immune infiltration in patients with LGG, and PSENEN may be a candidate prognostic biomarker for determining prognosis associated with immune infiltration in LGG.

Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease

Alzheimer's disease (AD), the most common neurodegenerative disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. Meanwhile, abnormalities in iron metabolism have been demonstrated in patients and mouse models with AD. Therefore, this study sought to find hub genes based on iron metabolism that can influence the diagnosis and treatment of AD. First, gene expression profiles were downloaded from the GEO database, including non-demented (ND) controls and AD samples. Fourteen iron metabolism-related gene sets were downloaded from the MSigDB database, yielding 520 iron metabolism-related genes. The final nine hub genes associated with iron metabolism and AD were obtained by differential analysis and WGCNA in brain tissue samples from GSE132903. GO analysis revealed that these genes were mainly involved in two major biological processes, autophagy and iron metabolism. Through stepwise regression and logistic regression analyses, we selected four of these genes to construct a diagnostic model of AD. The model was validated in blood samples from GSE63061 and GSE85426, and the AUC values showed that the model had a relatively good diagnostic performance. In addition, the immune cell infiltration of the samples and the correlation of different immune factors with these hub genes were further explored. The results suggested that these genes may also play an important role in immunity to AD. Finally, eight drugs targeting these nine hub genes were retrieved from the DrugBank database, some of which were shown to be useful for the treatment of AD or other concomitant conditions, such as insomnia and agitation. In conclusion, this model is expected to guide the diagnosis of patients with AD by detecting the expression of several genes in the blood. These hub genes may also assist in understanding the development and drug treatment of AD.