A signature of 14 immune-related gene pairs predicts overall survival in gastric cancer

Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These profiles represent human NASH and control samples and were used for differential genes (DEGs) expression screening. Two machine learning methods, the Least Absolute Shrinkage and Selection Operator regression model and Support Vector Machine Recursive Feature Elimination, were used to identify candidate disease signature genes. The CIBERSORT deconvolution algorithm was employed to analyze the infiltration of 22 immune cell types in NASH. Additionally, we constructed a NASH cell model using HepG2 cells treated with oleic acid and free fatty acids. The construction of the cell model was verified using oil red O staining, and Western blotting was used to detect the protein expression of the disease signature genes in both control and model groups. As a result, a total of 262 DEGs were identified. These DEGs were primarily associated with metal ion transmembrane transporter activity, sodium ion transmembrane transporter protein activity, calcium ion, and neuroactive ligand-receptor interactions. FOS, IGFBP2, dual-specificity phosphatase 1 (DUSP1), and IKZF3 were identified as disease signature genes of NASH by the least absolute shrinkage and selection operator and Support Vector Machine Recursive Feature Elimination algorithms for DEGs analysis. The receiver operating characteristic curves showed that FOS, IGFBP2, DUSP1, and IKZF3 had good diagnostic value (area under receiver operating characteristic curve > 0.8). These findings were validated in the GSE89632 dataset and through cellular assays. Immunocyte infiltration analysis revealed that NASH was associated with CD8 T cells, CD4 T cells, follicular helper T cells, resting NK cells, eosinophils, regulatory T cells, and γδ T cells. The FOS, IGFBP2, DUSP1, and IKZF3 genes were specifically associated with follicular helper T cells. Lipid droplet aggregation significantly increased in HepG2 cells treated with oleic acid and free fatty acids, indicating successful construction of the cell model. In this model, the expression of FOS, IGFBP2, and DUSP1 was significantly decreased, while that of IKZF3 was significantly elevated (P < .01, P < .001) compared with the control group. Therefore, FOS, IGFBP2, DUSP1, and IKZF3 can be considered as disease signature genes associated with immune infiltration in NASH.

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

Background

Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body's anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors.

Methods

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments.

Results

GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells.

Conclusion

This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.

Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis

Introduction

Sepsis is a worldwide epidemic, with high morbidity and mortality. Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimed to explore genes associated with cuproptosis in sepsis, construct predictive models and screen for potential targets.

Methods

The LASSO algorithm and SVM-RFE model has been analysed the expression of cuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networks and PPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples.

Results

We found 381 genes associated with the development of sepsis and discovered significantly differentially expressed cuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower. NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A and DLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes. The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis.

Discussion

Our study systematically explored the relationship between sepsis and cuproptosis and constructed a diagnostic model. And, several cuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.

XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis

X-ray repair cross-complementation group 1 (XRCC1) is a pivotal contributor to base excision repair, and its dysregulation has been implicated in the oncogenicity of various human malignancies. However, a comprehensive pan-cancer analysis investigating the prognostic value, immunological functions, and epigenetic associations of XRCC1 remains lacking. To address this knowledge gap, we conducted a systematic investigation employing bioinformatics techniques across 33 cancer types. Our analysis encompassed XRCC1 expression levels, prognostic and diagnostic implications, epigenetic profiles, immune and molecular subtypes, Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), immune checkpoints, and immune infiltration, leveraging data from TCGA, GTEx, CELL, Human Protein Atlas, Ualcan, and cBioPortal databases. Notably, XRCC1 displayed both positive and negative correlations with prognosis across different tumors. Epigenetic analysis revealed associations between XRCC1 expression and DNA methylation patterns in 10 cancer types, as well as enhanced phosphorylation. Furthermore, XRCC1 expression demonstrated associations with TMB and MSI in the majority of tumors. Interestingly, XRCC1 gene expression exhibited a negative correlation with immune cell infiltration levels, except for a positive correlation with M1 and M2 macrophages and monocytes in most cancers. Additionally, we observed significant correlations between XRCC1 and immune checkpoint gene expression levels. Lastly, our findings implicated XRCC1 in DNA replication and repair processes, shedding light on the precise mechanisms underlying its oncogenic effects. Overall, our study highlights the potential of XRCC1 as a prognostic and immunological pan-cancer biomarker, thereby offering a novel target for tumor immunotherapy.

Identification of the Key Genes of Immune Infiltration in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of heart failure. In this study, we screened the immune infiltration-related genes associated with DCM to explore the potential molecular mechanisms and provide a basis for the early diagnosis and development of new immunotherapeutic targets. A dataset related to DCM was downloaded from the Gene Expression Omnibus (GEO) database. R software was applied to the genetic differential analysis of patients with DCM and healthy individuals, and the obtained differential expressed genes (DEGs) were screened for differentially expressed immune-related genes (DEIRGs) after comparison with the immune microsatellite database. Gene functional analysis established a protein interaction network (PPI). The immune infiltration in patients with DCM versus normal controls was assessed using the CIBERSORT algorithm, the hub genes were screened using the MOCDE app, and the hubs were validated in multiple datasets. A total of 246 DEGs were screened (adj. P < 0.05 and |logFC| > 0.3), and a total of 170 DEIRGs were compared. Gene Ontology analysis showed significant (adj. P < 0.05) Biological Process entries of 591, Cellular Component of 10, and Molecular Function of 39; Kyoto Encyclopedia of Genes and Genomes showed 20 significant entries, mainly focused on cytokines involved in immune-related response, etc. A protein interaction network comprising 28 hub DEGs was constructed in combination with the PPI network interactions. DEIRG was mainly distributed in the T-cell receptor pathway by immune infiltration detection analysis, and significant changes in central memory T-cells were found by analyzing T-cell-related subpathways, where INSR, HLA-B, IFITM1, and HBEGF were significantly differentially expressed. We selected 632 hospitalized patients for validation and found that INSR and HLA-B expression were associated with DCM development by Nomogram. The expression of HLA-B in peripheral blood T-cells was higher in DCM patients than in the normal group, as verified by qRT-PCR. However, the detailed mechanism needs to be further explored.

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA

Background

Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.

Methods

The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.

Results

A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.

Conclusion

The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.

Association of the LEP gene with immune infiltration as a diagnostic biomarker in preeclampsia

Objective: Preeclampsia (PE) is a serious condition in pregnant women and hence an important topic in obstetrics. The current research aimed to recognize the potential and significant immune-related diagnostic biomarkers for PE. Methods: From the Gene Expression Omnibus (GEO) data sets, three public gene expression profiles (GSE24129, GSE54618, and GSE60438) from the placental samples of PE and normotensive pregnancy were downloaded. Differentially expressed genes (DEGs) were selected and determined among 73 PE and 85 normotensive control pregnancy samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the expression levels and diagnostic value of biomarkers in PE were verified in the GSE75010 data set (80 PE and 77 controls) and validated by qRT-RCR, Western blot, and immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in PE. Results: In total, 15 DEGs were recognized. The GO and KEGG analyses revealed that the DEGs were enriched in the steroid metabolic process, receptor ligand activity, GnRH secretion, and neuroactive ligand-receptor interaction. The recognized DEGs were primarily implicated in cell-type benign neoplasm, kidney failure, infertility, and PE. Gene sets related to hormone activity, glycosylation, multicellular organism process, and response to BMP were activated in PE. The LEP gene was distinguished as a diagnostic biomarker of PE (AUC = 0.712) and further certified in the GSE75010 data set (AUC = 0.850). The high expression of LEP was associated with PE in clinical samples. In addition, the analysis of the immune microenvironment showed that gamma delta T cells, memory B cells, M0 macrophages, and regulatory T cells were positively correlated with LEP expression (P < 0.05). Conclusion: LEP expression can be considered to be a diagnostic biomarker of PE and can offer a novel perspective for future studies regarding the occurrence and molecular mechanisms of PE.

A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer

BACKGROUND

Identifying reliable biomarkers could effectively predict esophagus carcinoma (EC) patients with poor prognosis. In this work, we constructed an immune-related gene pairs (IRGP) signature to evaluate the prognosis of EC.

RESULTS

The IRGP signature was trained by the TCGA cohort and validated by three GEO datasets, respectively. Cox regression model together with LASSO was applied to construct the overall survival (OS) associated IRGP. 21 IRGPs consisting of 38 immune-related genes were included in our signature, according to which patients were stratified into high- and low-risk groups. The results of Kaplan-Meier survival analyses indicated that high-risk EC patients had worse OS than low-risk group in the training set, meta-validation set and all independent validation datasets. After adjustment in multivariate Cox analyses, our signature continued to be an independent prognostic factor of EC and the signature-based nomogram could effectively predict the prognosis of EC sufferers. Besides, Gene Ontology analysis revealed this signature is related to immunity. 'CIBERSORT' analysis revealed the infiltration levels of plasma cells and activated CD4 memory T cells in two risk groups were significantly different. Ultimately, we validated the expression levels of six selected genes from IRGP index in KYSE-150 and KYSE-450.

CONCLUSIONS

This IRGP signature could be applied to select EC patients with high mortality risk, thereby improving prospects for the treatment of EC.

Analysis of potential biomarkers and immune infiltration in autism based on bioinformatics analysis

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by both environmental and genetic factors. However, its etiology and pathogenesis remain unclear. The purpose of this study was to establish an immune-related diagnostic model for ASD using bioinformatics methods and to identify ASD biomarkers. Two ASD datasets, GSE18123 and GSE29691, were integrated into the gene expression Database to eliminate batch effects. 41 differentially expressed genes were identified by microarray data linear model (limma package). Based on the results of the immune infiltration analysis, we speculated that neutrophils, B cells naive, CD8+ T cells, and Tregs are potential core immune cells in ASD and participate in the occurrence of ASD. Finally, the differential genes and immune infiltration in ASD and non-ASD patients were compared, and the most relevant genes were selected to construct the first immune correlation prediction model of ASD. After the calculation, the model exhibited better accuracy. The calculations show that the model has good accuracy.

An immune-related prognostic signature associated with immune landscape and therapeutic responses in gastric cancer

Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets in various tumors. However, the role of IRGs in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features characterizing the IRGs in GC. Data were acquired from the TCGA and GEO databases. The Cox regression analyses were performed to develop a prognostic risk signature. The genetic variants, immune infiltration, and drug responses associated with the risk signature were explored using bioinformatics methods. Lastly, the expression of the IRS was verified by qRT-PCR in cell lines. In this manner, an immune-related signature (IRS) was established based on 8 IRGs. According to the IRS, patients were divided into the low-risk group (LRG) and high-risk group (HRG). Compared with the HRG, the LRG was characterized by a better prognosis, high genomic instability, more CD8+ T cell infiltration, greater sensitivity to chemotherapeutic drugs, and greater likelihood of benefiting from the immunotherapy. Moreover, the expression result showed good consistency between the qRT-PCR and TCGA cohort. Our findings provide insights into the specific clinical and immune features underlying the IRS, which may be important for patient treatment.

Senescence-related genes define prognosis, immune contexture, and pharmacological response in gastric cancer

As one of the prevalent tumors worldwide, gastric cancer (GC) has obtained sufficient attention in its clinical management and prognostic stratification. Senescence-related genes are involved in the tumorigenesis and progression of GC. A machine learning algorithm-based prognostic signature was developed from six senescence-related genes including SERPINE1, FEN1, PDGFRB, SNCG, TCF3, and APOC3. The TCGA-STAD cohort was utilized as a training set while the GSE84437 and GSE13861 cohorts were analyzed for validation. Immune cell infiltration and immunotherapy efficacy were investigated in the PRJEB25780 cohort. Data from the genomics of drug sensitivity in cancer (GDSC) database revealed pharmacological response. The GSE13861 and GSE54129 cohorts, single-cell dataset GSE134520, and The Human Protein Atlas (THPA) database were utilized for localization of the key senescence-related genes. Association of a higher risk-score with worse overall survival (OS) was identified in the training cohort (TCGA-STAD, P<0.001; HR = 2.03, 95% CI, 1.45-2.84) and the validation cohorts (GSE84437, P = 0.005; HR = 1.48, 95% CI, 1.16-1.95; GSE13861, P = 0.03; HR = 2.23, 95% CI, 1.07-4.62). The risk-score was positively correlated with densities of tumor-infiltrating immunosuppressive cells (P < 0.05) and was lower in patients who responded to pembrolizumab monotherapy (P = 0.03). Besides, patients with a high risk-score had higher sensitivities to the inhibitors against the PI3K-mTOR and angiogenesis (P < 0.05). Expression analysis verified the promoting roles of FEN1, PDGFRB, SERPINE1, and TCF3, and the suppressing roles of APOC3 and SNCG in GC, respectively. Immunohistochemistry staining and single-cell analysis revealed their location and potential origins. Taken together, the senescence gene-based model may potentially change the management of GC by enabling risk stratification and predicting response to systemic therapy.

Biomarkers Associated with Immune Checkpoint, N6-Methyladenosine, and Ferroptosis in Patients with Restenosis

Purpose

This study aimed to identify potential diagnostic markers of restenosis after stent implantation and to determine their association with immune checkpoint, ferroptosis, and N6-methyladenosine (m6A).

Patients and methods

Microarray data were downloaded from the National Center for Biotechnology Information (NCBI: GSE46560 and GSE48060 datasets) to identify differentially expressed genes (DEGs) between in-stent restenosis and no-restenosis samples. We then conducted systematic functional enrichment analyses of the DEGs based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and further predicted the interactions of different proteins using the Search Tool for the Retrieval of Interacting Genes (STRING). We used the MCC and MCODE algorithms in the cytoHubba plug-in to screen three key genes in the network, and employed receiver operating characteristic (ROC) curves to determine their diagnostic significance using a multiscale curvature classification algorithm. Next, we investigated the relationships between these target genes, immune checkpoint, ferroptosis, and m6A. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the above results.

Results

We identified 62 upregulated genes and 243 downregulated genes. Based on GO, KEGG, and screening results, EEF1D, RPL36, and RPSA are promising genes for predicting restenosis. In addition, the methylation of YTHDF2, the ferroptosis-related gene GLS2, and the immune checkpoint-related gene CTLA4 were observed to be associated with restenosis. The qRT-PCR test confirmed that RPSA and RPL36 are useful diagnostic markers of the restenosis that can provide new insights for future studies on its occurrence and molecular mechanisms.

Conclusion

We found that RPSA and RPL36, as useful diagnostic markers of restenosis, can provide new insights for future studies on its occurrence and molecular mechanisms.

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer. Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential. Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups. Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer.

DPP6 and MFAP5 are associated with immune infiltration as diagnostic biomarkers in distinguishing uterine leiomyosarcoma from leiomyoma

Objective

Uterine leiomyosarcoma (ULMS) is the most common subtype of uterine sarcoma and is difficult to discern from uterine leiomyoma (ULM) preoperatively. The aim of the study was to determine the potential and significance of immune-related diagnostic biomarkers in distinguishing ULMS from ULM.

Methods

Two public gene expression profiles (GSE36610 and GSE64763) from the GEO datasets containing ULMS and ULM samples were downloaded. Differentially expressed genes (DEGs) were selected and determined among 37 ULMS and 25 ULM control samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Disease Ontology (DO) enrichment analyses as well as gene set enrichment analysis (GSEA). The candidate biomarkers were identified by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the biomarker expression levels and diagnostic value in ULMS were verified in the GSE9511 and GSE68295 datasets (12 ULMS and 10 ULM), and validated by immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in ULMS.

Result

In total, 55 DEGs were recognized via GO analysis, and KEGG analyses revealed that the DEGs were enriched in nuclear division, and cell cycle. The recognized DEGs were primarily implicated in non-small cell lung carcinoma and breast carcinoma. Gene sets related to the cell cycle and DNA replication were activated in ULMS. DPP6 and MFAP5 were distinguished as diagnostic biomarkers of ULMS (AUC = 0.957, AUC = 0.899, respectively), and they were verified in the GSE9511 and GSE68295 datasets (AUC = 0.983, AUC = 0.942, respectively). The low expression of DPP6 and MFAP5 were associated with ULMS. In addition, the analysis of the immune microenvironment indicated that resting mast cells were positively correlated with DPP6 and MFAP5 expression and that eosinophils and M0 macrophages were negatively correlated with DPP6 expression (P<0.05).

Conclusion

These findings indicated that DPP6 and MFAP5 are diagnostic biomarkers of ULMS, thereby offering a novel perspective for future studies on the occurrence, function and molecular mechanisms of ULMS.

Revealing immune infiltrate characteristics and potential diagnostic value of immune-related genes in ulcerative colitis: An integrative genomic analysis

Objectives

Ulcerative colitis (UC) is an autoimmune disease of the colon. The aim of this study was to explore the characteristics of immune infiltrates in UC patients and identify immune-related diagnostic biomarkers for UC.

Methods

Three gene expression profiles were acquired from the GEO database, followed by identification of differentially expressed genes (DEGs) by Linear Modeling of Microarray Data. Enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Disease Ontology (DO) were performed to analyze the biological functions of DEGs. Subsequently, the single sample gene set enrichment analysis (ssGSEA) was performed to identify immune infiltration characteristics of UC. Correlations between diagnostic genes and immune infiltration were explored to identify markers with the greatest diagnostic potential, and a UC diagnostic model was subsequently constructed. Finally, the prediction performance of the model was quantified by nomogram, non-correlated nomogram, and ROC curve.

Results

A total of 3111 DEGs (1,608 up-regulated and 1,503 down-regulated genes) were identified. DEGs were significantly involved in the immune system and UC-related pathways. Immune infiltration profiles of colonic tissue were significantly different between healthy individuals and UC patients. High proportions of resting of aDCs, B cells, CD8⁺ T cells, DCs, iDCs, Macrophages, Neutrophils, pDCs, T helper cells, Tfh, Th1 cells, Th2 cells, TIL and Treg were found in UC samples. A 5-gene based diagnostic prediction model was constructed and the results of nomogram, non-correlated nomogram and ROC curve suggested the powerful diagnostic value of the model.

Conclusions

This study identified the immune infiltrate characteristics and 5 immune-related genes for UC. The model based on the immune-related genes facilitates the early diagnosis of UC and provides a basis for the evaluation of the prognosis of UC.

Machine learning-based characterization of cuprotosis-related biomarkers and immune infiltration in Parkinson's disease

Background: Parkinson's disease (PD) is a neurodegenerative disease commonly seen in the elderly. On the other hand, cuprotosis is a new copper-dependent type of cell death that can be observed in various diseases. Methods: This study aimed to identify potential novel biomarkers of Parkinson's disease by biomarker analysis and to explore immune cell infiltration during the onset of cuprotosis. Gene expression profiles were retrieved from the GEO database for the GSE8397, GSE7621, GSE20163, and GSE20186 datasets. Three machine learning algorithms: the least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE) were used to screen for signature genes for Parkinson's disease onset and cuprotosis-related genes (CRG). Immune cell infiltration was estimated by ssGSEA, and cuprotosis-related genes associated with immune cells and immune function were examined using spearman correlation analysis. Nomogram was created to validate the accuracy of these cuprotosis-related genes in predicting PD disease progression. Classification of Parkinson's specimens using consensus clustering methods. Result: Three PD datasets from the Gene Expression Omnibus (GEO) database were combined after eliminating batch effects. By ssGSEA, we identified three cuprotosis-related genes ATP7A, SLC31A1, and DBT associated with immune cells or immune function in PD and more accurate for the diagnosis of Parkinson's disease course. Patients could benefit clinically from a characteristic line graph based on these genes. Consistent clustering analysis identified two subtypes, with the C2 subtype exhibiting higher immune cell infiltration and immune function. Conclusion: In conclusion, our study reveals that several newly identified cuprotosis-related genes intervene in the progression of Parkinson's disease through immune cell infiltration.

Classification of stomach adenocarcinoma based on fatty acid metabolism-related genes frofiling

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear.

Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample.

Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105–1.240, p &lt; 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 (GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy.

Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.

Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer

Gastric cancer (GC) is one of the most common tumors in the world, and apoptosis is closely associated with GC. A number of therapeutic methods have been implemented to increase the survival in GC patients, but the outcomes remain unsatisfactory. Apoptosis is a highly conserved form of cell death, but aberrant regulation of the process also leads to a variety of major human diseases. As variations of apoptotic genes may increase susceptibility to gastric cancer. Thus, it is critical to identify novel and potent tools to predict the overall survival (OS) and treatment efficacy of GC. The expression profiles and clinical characteristics of TCGA-STAD and GSE15459 cohorts were downloaded from TCGA and GEO. Apoptotic genes were extracted from the GeneCards database. Apoptosis risk scores were constructed by combining Cox regression and LASSO regression. The GSE15459 and TCGA internal validation sets were used for external validation. Moreover, we explored the relationship between the apoptosis risk score and clinical characteristics, drug sensitivity, tumor microenvironment (TME) and tumor mutational burden (TMB). Finally, we used GSVA to further explore the signaling pathways associated with apoptosis risk. By performing TCGA-STAD differential analysis, we obtained 839 differentially expressed genes, which were then analyzed by Cox regressions and LASSO regression to establish 23 genes associated with apoptosis risk scores. We used the test validation cohort from TCGA-STAD and the GSE15459 dataset for external validation. The AUC values of the ROC curve for 2-, 3-, and 5-years survival were 0.7, 0.71, and 0.71 in the internal validation cohort from TCGA-STAD and 0.77, 0.74, and 0.75 in the GSE15459 dataset, respectively. We constructed a nomogram by combining the apoptosis risk signature and some clinical characteristics from TCGA-STAD. Analysis of apoptosis risk scores and clinical characteristics demonstrated notable differences in apoptosis risk scores between survival status, sex, grade, stage, and T stage. Finally, the apoptosis risk score was correlated with TME characteristics, drug sensitivity, TMB, and TIDE scores.

Prediction of Immune Infiltration Diagnostic Gene Biomarkers in Kawasaki Disease

Kawasaki disease (KD) is characterized by disorder of immune response with unknown etiology. Immune cells may be closely related to the onset of KD. The focus of this research was to evaluate the significance of the infiltration of immune cells for this disease and find possible diagnostic biomarkers for KD. The Gene Expression Omnibus database was utilized to retrieve two freely accessible gene expression patterns (GSE68004 and GSE18606 datasets) from human KD and control specimens. 114 KD, as well as 46 control specimens, were searched for obtaining differentially expressed genes (DEGs). Candidate biological markers were determined utilizing the support vector machine recursive feature elimination and the least absolute shrinkage and selection operator regression model analysis. To assess discriminating capacity, the area under the receiver operating characteristic curve (AUC) was computed. The GSE73461 dataset was utilized to observe the biomarkers' expression levels and diagnostic significance in KD (78 KD patients and 55 controls). CIBERSORT was employed to assess the composition profiles of the 22 subtypes of immune cell fraction in KD on the basis of combined cohorts. 37 genes were discovered. The DEGs identified were predominantly involved in arteriosclerotic cardiovascular disease, atherosclerosis, autoimmune disease of the urogenital tract, and bacterial infectious disease. Gene sets related to complement and coagulation cascades, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis, NOD-like receptor signaling pathway, and regulation of actin cytoskeleton underwent differential activation in KD as opposed to the controls. KD diagnostic biomarkers, including the alkaline phosphatase (ALPL), endoplasmic reticulum degradation-enhancing alpha-mannosidase-like protein 2 (EDEM2), and histone cluster 2 (HIST2H2BE), were discovered (AUC = 1.000) and verified utilizing the GSE73461 dataset (AUC = 1.000). Analyses of immune cell infiltration demonstrated that ALPL, EDEM2, and HIST2H2BE were linked to CD4 memory resting T cells, monocytes, M0 macrophages, CD8 T cells, neutrophils, and memory CD4 T cells. ALPL, EDEM2, and HIST2H2BE could be utilized as KD diagnostic indicators, and they can also deliver useful information for future research on the disease's incidence and molecular processes.

LncRNA HAR1A Suppresses the Development of Non-Small Cell Lung Cancer by Inactivating the STAT3 Pathway

Simple Summary

We found that lncRNA Highly Accelerated Region 1A (HAR1A) was down regulated in NSCLC. Moreover, a 23-gene signature derived from HAR1A-related cancer cell survival genes could predict prognosis and chemotherapy response in LUAD. In vitro experiments indicated that HAR1A suppressed NSCLC growth by inhibiting the STAT3 signaling pathway, which was verified in the animal model. Overall, HAR1A acts as a tumor suppressor in NSCLC. The prognostic signature showed promise in predicting prognosis and chemotherapy sensitivity.

Abstract

It is imperative to advance the understanding of lung cancer biology. The Cancer Genome Atlas (TCGA) dataset was used for bioinformatics analysis. CCK-8 assay, flow cytometry, and western blot were performed in vitro, followed by in vivo study. We found that lncRNA Highly Accelerated Region 1A (HAR1A) is significantly downregulated in lung adenocarcinoma (LUAD) and negatively associated with prognosis. We improved the prognostic accuracy of HAR1A in LUAD by combining genes regulating cell apoptosis and cell cycle to generate a 23-gene signature. Nomogram and decision curve analysis (DCA) confirmed that the gene signature performed robustly in predicting overall survival. Gene set variation analysis (GSVA) demonstrated several significantly upregulated malignancy-related events in the high-risk group, including DNA replication, DNA repair, glycolysis, hypoxia, MYC targets v2, and mTORC1. The risk signature distinguished LUAD patients suitable for chemotherapies or targeted therapies. Additionally, the knockdown of HAR1A accelerated NSCLC cell proliferation but inhibited apoptosis and vice versa. HAR1A regulated cellular activities through the STAT3 signaling pathway. The tumor-suppressing role of HAR1A was verified in the mouse model. Overall, the gene signature was robustly predictive of prognosis and sensitivity to anti-tumor drugs. HAR1A functions as a tumor suppressor in NSCLC by regulating the STAT3 signaling pathway.

Dissecting the Role of Immune Checkpoint Regulation Patterns in Tumor Microenvironment and Prognosis of Gastric Cancer

Many studies suggest that immune checkpoint molecules play a vital role in tumor progression and immune responses. However, the impact of the comprehensive regulation pattern of immune checkpoint molecules on immune responses, tumor microenvironment (TME) formation, and patient prognosis is poorly understood. In this study, we evaluated immune checkpoint regulation patterns in 1,174 gastric cancer (GC) samples based on 31 immune checkpoint genes (ICGs). Three distinct immune checkpoint regulation patterns with significant prognostic differences were ultimately identified. Moreover, GC patients were divided into two subgroups according to immune checkpoint score (ICscore). Patients with lower ICscore were characterized by a favorable prognosis and enhanced immune infiltration as well as an increased tumor mutation burden, non-recurrence, and microsatellite instability-high. Collectively, this study indicated that immune checkpoint regulation patterns were essential to forming the diversity of TME and a better understanding of that will contribute to assessing the characteristics of TME in GC, which intends to improve the development of immunotherapy.

Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease

Objective

This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis.

Methods

The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results.

Results

80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells.

Conclusion

STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.

Integrated Analysis of Multi-Omics Alteration, Immune Profile, and Pharmacological Landscape of Pyroptosis-Derived lncRNA Pairs in Gastric Cancer

Background: Recent evidence demonstrates that pyroptosis-derived long non-coding RNAs (lncRNAs) have profound impacts on the initiation, progression, and microenvironment of tumors. However, the roles of pyroptosis-derived lncRNAs (PDLs) in gastric cancer (GC) remain elusive.

Methods: We comprehensively analyzed the multi-omics data of 839 GC patients from three independent cohorts. The previous gene set enrichment analysis embedding algorithm was utilized to identify PDLs. A gene pair pipeline was developed to facilitate clinical translation via qualitative relative expression orders. The LASSO algorithm was used to construct and validate a pyroptosis-derived lncRNA pair prognostics signature (PLPPS). The associations between PLPPS and multi-omics alteration, immune profile, and pharmacological landscape were further investigated.

Results: A total of 350 PDLs and 61,075 PDL pairs in the training set were generated. Cox regression revealed 15 PDL pairs associated with overall survival, which were utilized to construct the PLPPS model via the LASSO algorithm. The high-risk group demonstrated adverse prognosis relative to the low-risk group. Remarkably, genomic analysis suggested that the lower tumor mutation burden and gene mutation frequency (e.g., TTN, MUC16, and LRP1B) were found in the high-risk group patients. The copy number variants were not significantly different between the two groups. Additionally, the high-risk group possessed lower immune cell infiltration abundance and might be resistant to a few chemotherapeutic drugs (including cisplatin, paclitaxel, and gemcitabine).

Conclusion: PDLs were closely implicated in the biological process and prognosis of GC, and our PLPPS model could serve as a promising tool to advance prognostic management and personalized treatment of GC patients.

Identification of a novel m6A-related lncRNA pair signature for predicting the prognosis of gastric cancer patients

Background

Accumulating studies have demonstrated that lncRNAs play vital roles in the prognosis of gastric cancer (GC); however, the prognostic value of N6-methyladenosine-related lncRNAs has not been fully reported in GC. This study aimed to construct and validate an m6A-related lncRNA pair signature (m6A-LPS) for predicting the prognosis of GC patients.

Methods

GC cohort primary data were downloaded from The Cancer Genome Atlas. We analysed the coexpression of m6A regulators and lncRNAs to identify m6A-related lncRNAs. Based on cyclical single pairing along with a 0-or-1 matrix and least absolute shrinkage and selection operator-penalized regression analyses, we constructed a novel prognostic signature of m6A-related lncRNA pairs with no dependence upon specific lncRNA expression levels. All patients were divided into high-risk and low-risk group based on the median risk score. The predictive reliability was evaluated in the testing dataset and whole dataset with receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis was used to identify potential pathways.

Results

Fourteen m6A-related lncRNA pairs consisting of 25 unique lncRNAs were used to construct the m6A-LPS. Kaplan–Meier analysis showed that the high-risk group had poor prognosis. The area under the curve for 5-year overall survival was 0.906, 0.827, and 0.882 in the training dataset, testing dataset, and whole dataset, respectively, meaning that the m6A-LPS was highly accurate in predicting GC patient prognosis. The m6A-LPS served as an independent prognostic factor for GC patients after adjusting for other clinical factors (p < 0.05). The m6A-LPS had more accuracy and a higher ROC value than other prognostic models for GC. Functional analysis revealed that high-risk group samples mainly showed enrichment of extracellular matrix receptor interactions and focal adhesion. Moreover, N-cadherin and vimentin, known biomarkers of epithelial–mesenchymal transition, were highly expressed in high-risk group samples. The immune infiltration analysis showed that resting dendritic cells, monocytes, and resting memory CD4 T cells were significantly positively related to the risk score. Thus, m6A-LPS reflected the infiltration of several types of immune cells.

Conclusions

The signature established by pairing m6A-related lncRNAs regardless of expression levels showed high and independent clinical prediction value in GC patients.

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

Background

Gastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.

Methods

In this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.

Results

We acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.

Conclusions

Our novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02493-2.

Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma

The tumor immune microenvironment plays essential roles in regulating inflammation, angiogenesis, immune modulation, and sensitivity to therapies. Here, we developed a powerful prognostic signature with immune-related lncRNAs (irlncRNAs) in lung adenocarcinoma (LUAD). We obtained differentially expressed irlncRNAs by intersecting the transcriptome dataset for The Cancer Genome Atlas (TCGA)-LUAD cohort and the ImmLnc database. A rank-based algorithm was applied to select top-ranking altered irlncRNA pairs for the model construction. We built a prognostic signature of 33 irlncRNA pairs comprising 40 unique irlncRNAs in the TCGA-LUAD cohort (training set). The immune signature significantly dichotomized LUAD patients into high- and low-risk groups regarding overall survival, which is likewise independently predictive of prognosis (hazard ratio = 3.580, 95% confidence interval = 2.451-5.229, P < 0.001). A nomogram with a C-index of 0.79 demonstrates the superior prognostic accuracy of the signature. The prognostic accuracy of the signature of 33 irlncRNA pairs was validated using the GSE31210 dataset (validation set) from the Gene Expression Omnibus database. Immune cell infiltration was calculated using ESTIMATE, CIBERSORT, and MCP-count methodologies. The low-risk group exhibited high immune cell infiltration, high mutation burden, high expression of CTLA4 and human leukocyte antigen genes, and low expression of mismatch repair genes, which predicted response to immunotherapy. Interestingly, pRRophetic analysis demonstrated that the high-risk group possessed reverse characteristics was sensitive to chemotherapy. The established immune signature shows marked clinical and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in LUAD.

Identification of an Immune-Related Long Noncoding RNA Pairs Model to Predict Survival and Immune Features in Gastric Cancer

Background: Gastric cancer (GC) remains one of the most malignant tumors around the world, and an accurate model that reliably predicts survival and therapeutic efficacy is urgently needed. As a novel predictor for prognosis in a variety of cancers, immune-related long noncoding RNA pairs (IRlncRNAPs) have been reported to predict tumor prognosis. Herein, we integrated an IRlncRNAPs model to predict the clinical outcome, immune features, and chemotherapeutic efficacy of GC.

Methods: Based on the GC data obtained from The Cancer Genome Atlas (TCGA) database and the Immunology Database and Analysis Portal (ImmPort), differentially expressed immune-related long noncoding RNAs (DEIRlncRNAs) were identified. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis were used to select the most appropriate overall survival (OS)-related IRlncRNAPs to develop a prognostic signature. The riskScore of each sample was calculated by comparing the long noncoding RNA expression level in each IRlncRNAP. Based on the riskScore for each patient, GC patients were divided into high- and low-risk groups. Then, the correlation of the signature and riskScore with OS, clinical features, immune cell infiltration, immune-related gene (IRG) expression and chemotherapeutic efficacy in GC was analyzed.

Results: A total of 107 DEIRlncRNAs were identified which formed 4297 IRlncRNAPs. Fifteen OS-related IRlncRNAPs were selected to develop a prognostic model. GC patients could be accurately classified into high- and low-risk groups according to the riskScore of the prognostic model. The 1-, 2-, 3-, and 5-year receiver operating characteristic (ROC) curves for the riskScore were drawn and the area under the curve (AUC) values were found to be 0.788, 0.810, 0.825, and 0.868, respectively, demonstrating a high sensitivity and accuracy of this prognostic signature. Moreover, the immune-related riskScore was an independent risk factor. Patients showed a poorer outcome within the high-risk group. In addition, the riskScore was found to be significantly correlated with the clinical features, immune infiltration status, IRG expression, and chemotherapeutic efficacy in GC.

Conclusion: The prognostic model of IRlncRNAPs offers great promise in predicting the prognosis, immune infiltration status, and chemotherapeutic efficacy in GC, which might be helpful for the selection of chemo- and immuno-therapy of GC.

N6-Methyladenosine Related Long Non-Coding RNAs and Immune Cell Infiltration in the Tumor Microenvironment of Gastric Cancer

Aim

To illustrate the influence of N6-methyladenosine long non-coding RNAs and immune cell infiltration in gastric cancer.

Methods

We downloaded workflow-type data and clinical data from The Cancer Genome Atlas project. The relationship of lncRNA and m6A was identified. Kyoto Encyclopedia of Genes and Genomes gene expression enrichment analysis was performed. Lasso regression was utilized to construct a prognostic model. Survival analysis to explore the relationship between m6A lncRNA and clinical survival data. Differential analysis of the tumor microenvironment and immune correlation analysis to determine immune cell infiltration levels and their correlation with clinical prognosis.

Results

Co-expression analysis indicated that lncRNA expression was associated closely with m6A. m6A-lncRNAs were partially highly expressed in tumor tissue and could be used in a prognostic model to predict GC prognosis, independent of other clinical characteristics. “ADIPPOCYTOKINE SIGNALING PATHWAY” was most significantly enriched according to GSEA. ACBD3-AS1 was overexpressed in tumor tissue. Naïve B cell, Plasma cells, resting CD4 memory T cell were highly infiltrated tissues in cluster 2, while Macrophages M2, resting Mast cells, Monocytes, regulates T cells were lowly in cluster 1. All related scores were higher in cluster 2, indicating a lower purity of tumor cells and higher density of immune-related cells in the tumor microenvironment.

Conclusion

m6A lncRNA is closely related to the occurrence and progression of GC. The corresponding prognostic model can be utilized to evaluate the prognosis of GC. m6A lncRNA and related immune cell infiltration in the tumor microenvironment can provide novel therapeutic targets for further research.

Clinical significance and prognostic role of an immune-related gene signature in gastric adenocarcinoma

Limited progress has been made in the treatment of gastric adenocarcinoma (GAC) in recent years, but the potential of immunotherapy in GAC is worthy of consideration. The purpose of this study was to develop a reliable, personalized signature based on immune genes to predict the prognosis of GAC. Here, we identified two groups of patients with significantly different prognoses by performing unsupervised clustering analysis of The Cancer Genome Atlas (TCGA) database based on 881 immune genes. The immune signature was constructed with a training set composed of 350 GAC samples from the TCGA and subsequently validated with 431 samples from GSE84437, 432 samples from GSE26253, and 145 GAC samples from real-time quantitative reverse transcription polymerase chain reaction data. This classification system can also be used to predict prognosis in different clinical subgroups. Further analysis suggested that high-risk patients were characterized by low immune scores, distinctive immune cell proportions, different immune checkpoint profiles, and a low tumor mutational burden. Ultimately, the signature was identified as an independent prognostic factor. In general, the signature can accurately predict recurrence and overall survival in patients with GAC and may serve as a powerful prognostic tool to further optimize cancer immunotherapy.

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes in Gastric Cancer

Background: Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism–related genes in GC patients.

Materials and Methods: Two GC datasets from the Gene Expression Atlas, GSE62254 (n = 300) and GSE26942 (n = 217), were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes was evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors.

Results: A risk predictive score model was established based on the expression of 19 lipid metabolism–related genes (LPL, IPMK, PLCB3, CDIPT, PIK3CA, DPM2, PIGZ, GPD2, GPX3, LTC4S, CYP1A2, GALC, SGMS1, SMPD2, SMPD3, FUT6, ST3GAL1, B4GALNT1, and ACADS). The time-dependent ROC analysis revealed that the risk predictive score model was stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A higher risk score was associated with more aggressive features, including a higher tumor grade, a more advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the high–risk and low–risk score groups. A nomogram containing patients’ age, tumor stage, adjuvant chemotherapy, and the risk predictive score could accurately predict the survival probability of patients at 1, 3, and 5 years.

Conclusion: A novel 19-gene risk predictive score model was developed based on the lipid metabolism–related genes, which could be a potential prognostic indicator and therapeutic target of GC.

Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis

Atrial fibrillation (AF) is the most common cardiac arrhythmia. This study aimed to identify potential novel biomarkers for persistent AF (pAF) using integrated analyses and explore the immune cell infiltration in this pathological process. Three pAF datasets (GSE31821, GSE41177, and GSE79768) from the Gene Expression Omnibus (GEO) database were integrated with the elimination of batch effects. 264 differentially expressed genes (DEGs) were identified using Linear models for microarray data (LIMMA), 12 modules were screened out by weighted gene co-expression network analysis (WGCNA) in pAF compared with normal controls. Subsequently, common genes (CGs) were identified as the intersection of DEGs and genes in the most significant module. Functional enrichment analysis showed that CGs were mainly enriched in the "Calcineurin-NFAT (nuclear factor of activated T-cells)" signaling pathway, particularly regulator of calcineurin 1 (RCAN1), and protein phosphatase 3 regulatory subunit B, alpha (PPP3R1). Ulteriorly, the microRNA-transcription factor-mRNA network revealed that microRNA-34a-5p could target both RCAN1 and PPP3R1 in the pAF pathogenesis. Finally, immune infiltration analysis by CIBERSORT, a versatile computational method, displayed a higher level of monocytes, dendritic cells and neutrophils, as well as a lower level of CD8+ T cells and T cells regulatory (Tregs) in pAF compared with the control group. In conclusion, our present study revealed several novel pAF-associated genes, miRNAs, and pathways, including microRNA-34a-5p, which might target RCAN1 and PPP3R1 to regulate pAF through the calcineurin-NFAT signaling pathway. In addition, there was a difference in immune infiltration between patients with pAF and normal groups and immune cells might interact with specific genes in pAF.

Discovery and Verification of an Immune-Related Gene Pairs Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma

The study of IRGPs to construct the prognostic signature in head and neck squamous cell carcinoma (HNSCC) has not yet elucidated. The objective of this study was to explore a novel model to predict the prognosis of HNSCC patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were set as training and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) and time-dependent ROC were employed to screen the highest frequency immune-related gene pairs (IRGPs) and their best cut-off value. Survival analysis, Cox regression analysis were applied to discover the effects of selected IRGPs signature on survival outcomes. The immune cell proportions were deconvoluted by the CIBERSORT method. After a couple of filtering, we obtained 22 highest frequency IRGPs. The overall survival time of HNSCC patients with a high score of IRGPs was shorter as compared to the ones with a low score in two independent datasets (P < 0.001). Six kinds of immune cells were found to be differentially distributed in the two different risk groups of HNSCC patients (P < 0.001). GO and GSEA analysis showed these differentially expressed genes enriched in multiple molecular functions. The new IRGPs signature probably confers a new insight into the prognosis prediction of HNSCC patients.

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Background

An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated.

Methods

Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300; GSE15459, n = 191; and GSE26901, n = 109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n = 600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432; GSE84437, n = 431; and TCGA, n = 336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method.

Results

A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC > 0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p < 0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p < 0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p = 0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p = 0.00085). The patients' risk score increased with the progression of the clinicopathological stage.

Conclusion

In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.

Identification and external validation of a prognostic signature associated with DNA repair genes in gastric cancer

The aim of this study was to construct and validate a DNA repair-related gene signature for evaluating the overall survival (OS) of patients with gastric cancer (GC). Differentially expressed DNA repair genes between GC and normal gastric tissue samples obtained from the TCGA database were identified. Univariate Cox analysis was used to screen survival-related genes and multivariate Cox analysis was applied to construct a DNA repair-related gene signature. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value. The prognostic model and the expression levels of signature genes were validated using an independent external validation cohort. Two genes (CHAF1A, RMI1) were identified to establish the prognostic signature and patients ware stratified into high- and low-risk groups. Patients in high-risk group presented significant shorter survival time than patients in the low-risk group in both cohorts, which were verified by the ROC curves. Multivariate analysis showed that the prognostic signature was an independent predictor for patients with GC after adjustment for other known clinical parameters. A nomogram incorporating the signature and known clinical factors yielded better performance and net benefits in calibration plot and decision curve analyses. Further, the logistic regression classifier based on the two genes presented an excellent diagnostic power in differentiating early HCC and normal tissues with AUCs higher than 0.9. Moreover, Gene Set Enrichment Analysis revealed that diverse cancer-related pathways significantly clustered in the high-risk and low-risk groups. Immune cell infiltration analysis revealed that CHAF1A and RMI1 were correlated with several types of immune cell subtypes. A prognostic signature using CHAF1A and RMI1 was developed that effectively predicted different OS rates among patients with GC. This risk model provides new clinical evidence for the diagnostic accuracy and survival prediction of GC.

A signature of 13 autophagy‑related gene pairs predicts prognosis in hepatocellular carcinoma

Growing evidences suggest that autophagy plays a momentous part in the tumorigenesis and development of hepatocellular carcinoma (HCC). However, there are not many researches to predict the prognosis of HCC using autophagy-related genes. Therefore, based on the clinical information and RNA-Seq data of The Cancer Genome Atlas data portal (TCGA), 13 autophagy‑related gene pairs were screened to build the autophagy‑related signature to predict the prognosis by least absolute shrinkage and selection operator (LASSO) regression analysis. Besides, the International Cancer Genome Consortium (ICGC) cohort was further applied to verify the autophagy‑related prognostic signature. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were also used to predict the relevant function of the autophagy-related gene pairs signature. As shown in the results, the autophagy-related gene pairs were mainly involved in process utilizing autophagic mechanism, autophagy, macroautophagy and cellular response to oxidative stress. The immune cell levels in the high-risk and low-risk group were explored, which showed that the three immune cells were obviously increased in the high-risk group, while the five immune cells were obviously increased in the low-risk group. In conclusion, an autophagy‑related prognostic signature was established to predict the prognosis of HCC patients with great accuracy and we found that autophagy‑related prognostic signature was related to infiltrating immune cells.

Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs

Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.

Development and validation of a hypoxia-related gene pair signature to predict overall survival in head and neck squamous cell carcinoma

OBJECTIVE

Head and neck squamous cell carcinoma (HNSCC) are a highly aggressive tumor with an extremely poor prognosis. Thus, we aimed to develop and validate a robust prognostic signature that can estimate the prognosis for HNSCC.

METHODS

Data on gene expressions and clinical were downloaded from TCGA and GEO database. To develop the best prognosis signature, a LASSO Cox Regression model was employed. Time-dependent receiver-operating characteristic (ROC) was used to determine the best cut-off value. Patients were divided into high-risk and low-risk hypoxia groups according to cut-off value. Survival differences were evaluated by log-rank test, while multivariate analysis was performed by a Cox proportional hazards model.

RESULTS

A 17-HRGPs composed of 24 unique genes was constructed, which was significantly related to OS. In the TCGA and GEO datasets, patients in the high hypoxia risk group have a poor prognosis (TCGA: P < 0.001, GEO: P < 0.05). After adjusting for other clinicopathological parameters, the 17-HRGP signature was independent prognostic factors in patients with HNSCC (P < 0.05). Functional analysis revealed that mRNA binding, gene silencing by RNA, RNA binding involved in posttranscriptional gene silencing signaling pathway were enriched in the low-risk groups. For this model, C-index was 0.684, which was higher than that of many established risk models. Macrophages M0, Mast cells activated, NK cells resting, T cells CD4 memory resting, etc. were significantly higher in the high-risk group, and B cells memory, Plasma cells, T cells follicular helper, T cells gamma delta, T cells CD8, etc. were significantly higher in the low-risk group.

CONCLUSION

In summary, our study constructed a robust HRGPs signature as molecular markers for predicting the outcome of HNSCC patients.

Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial Infarction

Objective: The present study was designed to identify potential diagnostic markers for acute myocardial infarction (AMI) and determine the significance of immune cell infiltration in this pathology.

Methods: Two publicly available gene expression profiles (GSE66360 and GSE48060 datasets) from human AMI and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 80 AMI and 71 control samples. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic curve (AUC) value was obtained and used to evaluate discriminatory ability. The expression level and diagnostic value of the biomarkers in AMI were further validated in the GSE60993 dataset (17 AMI patients and 7 controls). The compositional patterns of the 22 types of immune cell fraction in AMI were estimated based on the merged cohorts using CIBERSORT.

Results: A total of 27 genes were identified. The identified DEGs were mainly involved in carbohydrate binding, Kawasaki disease, atherosclerosis, and arteriosclerotic cardiovascular disease. Gene sets related to atherosclerosis signaling, primary immunodeficiency, IL-17, and TNF signaling pathways were differentially activated in AMI compared with the control. IL1R2, IRAK3, and THBD were identified as diagnostic markers of AMI (AUC = 0.877) and validated in the GSE60993 dataset (AUC = 0.941). Immune cell infiltration analysis revealed that IL1R2, IRAK3, and THBD were correlated with M2 macrophages, neutrophils, monocytes, CD4⁺ resting memory T cells, activated natural killer (NK) cells, and gamma delta T cells.

Conclusion: IL1R2, IRAK3, and THBD can be used as diagnostic markers of AMI, and can provide new insights for future studies on the occurrence and the molecular mechanisms of AMI.

Development and validation of a novel pseudogene pair-based prognostic signature for prediction of overall survival in patients with hepatocellular carcinoma

BACKGROUND

There is growing evidence that pseudogenes may serve as prognostic biomarkers in several cancers. The present study was designed to develop and validate an accurate and robust pseudogene pairs-based signature for the prognosis of hepatocellular carcinoma (HCC).

METHODS

RNA-sequencing data from 374 HCC patients with clinical follow-up information were obtained from the Cancer Genome Atlas (TCGA) database and used in this study. Survival-related pseudogene pairs were identified, and a signature model was constructed by Cox regression analysis (univariate and least absolute shrinkage and selection operator). All individuals were classified into high- and low-risk groups based on the optimal cutoff. Subgroups analysis of the novel signature was conducted and validated in an independent cohort. Pearson correlation analyses were carried out between the included pseudogenes and the protein-coding genes based on their expression levels. Enrichment analysis was performed to predict the possible role of the pseudogenes identified in the signature.

RESULTS

A 19-pseudogene pair signature, which included 21 pseudogenes, was established. Patients in high-risk group demonstrated an increased the risk of adverse prognosis in the TCGA cohort and the external cohort (all P < 0.001). The novel pseudogene signature was independent of other conventional clinical variables used for survival prediction in HCC patients in the two cohorts revealed by the multivariate Cox regression analysis (all P < 0.001). Subgroup analysis further demonstrated the diagnostic value of the signature across different stages, grades, sexes, and age groups. The C-index of the prognostic signature was 0.761, which was not only higher than that of several previous risk models but was also much higher than that of a single age, sex, grade, and stage risk model. Furthermore, functional analysis revealed that the potential biological mechanisms mediated by these pseudogenes are primarily involved in cytokine receptor activity, T cell receptor signaling, chemokine signaling, NF-κB signaling, PD-L1 expression, and the PD-1 checkpoint pathway in cancer.

CONCLUSION

The novel proposed and validated pseudogene pair-based signature may serve as a valuable independent prognostic predictor for predicting survival of patients with HCC.

Development and validation of a RNA binding protein gene pair-associated prognostic signature for prediction of overall survival in hepatocellular carcinoma

BACKGROUND

Increasing evidence has demonstrated the correlation between hepatocellular carcinoma (HCC) prognosis and RNA binding proteins (RBPs) dysregulation. Thus, we aimed to develop and validate a reliable prognostic signature that can estimate the prognosis for HCC.

METHODS

Gene expression profiling and clinical information of 374 HCC patients were derived from the TCGA data portal. The survival-related RBP pairs were determined using univariate cox-regression analysis and the signature was built based on LASSO analysis. All patients were divided patients into high-and low-risk groups according to the optimal cut off of the signature score determined by time-dependent receiver operating characteristic (ROC) curve analysis. The predictive value of the signature was further validated in an independent cohort.

RESULTS

A 37-RBP pairs signature consisting of 61 unique genes was constructed which was significantly associated with the survival. The RBP-related signature accurately predicted the prognosis of HCC patients, and patients in high-risk groups showed poor survival in two cohorts. The novel signature was an independent prognostic factor of HCC in two cohorts (all P < 0.001). Furthermore, the C-index of the prognostic model was 0.799, which was higher than that of many established risk models. Pathway and process enrichment analysis showed that the 61 unique genes were mainly enriched in translation, ncRNA metabolic process, RNA splicing, RNA modification, and translational termination.

CONCLUSION

The novel proposed RBP-related signature based on relative expression orderings could serve as a promising independent prognostic biomarker for patients with HCC, and could improve the individualized survival prediction in HCC.