Unveiling Immune Infiltration Characterizing Genes in Hypertrophic Cardiomyopathy Through Transcriptomics and Bioinformatics

Background

Hypertrophic cardiomyopathy (HCM) is a dominantly inherited disease associated with sudden immune cell associations that remain unclear. The aim of this study was to comprehensively screen candidate markers associated with HCM and immune cells and explore potential pathogenic pathways.

Methods

First, download the GSE32453 dataset to identify differentially expressed genes (DEGs) and perform Gene Ontology and pathway enrichment analysis using DAVID and GSEA. Next, construct protein-protein interaction (PPI) networks using String and Cytoscape to identify hub genes. Afterward, use CIBERSORT to determine the proportion of immune cells attributed to key genes in HCM and conduct ROC analysis based on the external dataset GSE36961 to evaluate their diagnostic value. Finally, validate the expression of key genes in the hypertrophic cardiomyocyte model through qRT-PCR using data from the HPA database.

Results

Comprehensive analysis revealed that there were 254 upregulated genes and 181 downregulated genes in HCM. The enrichment study underscored pathways of inflammatory signaling, including MAPK and PI3K-Akt pathways. Pathways abundant in genes associated with HCM encompassed myocardial contraction and NADH dehydrogenase activity. Additionally, the analysis of immune infiltration revealed a notable increase in macrophages, NK cells, and monocytes in the HCM group, showing statistically significant variances in CD4 memory resting T cell infiltration when compared to the healthy control group. Within the validation dataset GSE36961, the Area Under the Curve (AUC) scores for eight crucial genes (FOS, CD86, CD68, BDNF, PIK3R1, PLEK, RAC2, CCL2) each exceeded 0.8. The HPA database revealed the positioning traits and paths of these eight crucial genes in smooth muscle cells, myocardial cells, and fibroblasts. The outcomes of the qRT-PCR were aligned with the sequencing findings.

Conclusion

Bioinformatics analysis unveiled pivotal genes, pathways, and immune involvement, illuminating the molecular underpinnings of HCM. These findings suggest promising therapeutic targets for clinical applications.

Exploring the common mechanism of vascular dementia and inflammatory bowel disease: a bioinformatics-based study

Objective

Emerging evidence has shown that gut diseases can regulate the development and function of the immune, metabolic, and nervous systems through dynamic bidirectional communication on the brain-gut axis. However, the specific mechanism of intestinal diseases and vascular dementia (VD) remains unclear. We designed this study especially, to further clarify the connection between VD and inflammatory bowel disease (IBD) from bioinformatics analyses.

Methods

We downloaded Gene expression profiles for VD (GSE122063) and IBD (GSE47908, GSE179285) from the Gene Expression Omnibus (GEO) database. Then individual Gene Set Enrichment Analysis (GSEA) was used to confirm the connection between the two diseases respectively. The common differentially expressed genes (coDEGs) were identified, and the STRING database together with Cytoscape software were used to construct protein-protein interaction (PPI) network and core functional modules. We identified the hub genes by using the Cytohubba plugin. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify pathways of coDEGs and hub genes. Subsequently, receiver operating characteristic (ROC) analysis was used to identify the diagnostic ability of these hub genes, and a training dataset was used to verify the expression levels of the hub genes. An alternative single-sample gene set enrichment (ssGSEA) algorithm was used to analyze immune cell infiltration between coDEGs and immune cells. Finally, the correlation between hub genes and immune cells was analyzed.

Results

We screened 167 coDEGs. The main articles of coDEGs enrichment analysis focused on immune function. 8 shared hub genes were identified, including PTPRC, ITGB2, CYBB, IL1B, TLR2, CASP1, IL10RA, and BTK. The functional categories of hub genes enrichment analysis were mainly involved in the regulation of immune function and neuroinflammatory response. Compared to the healthy controls, abnormal infiltration of immune cells was found in VD and IBD. We also found the correlation between 8 shared hub genes and immune cells.

Conclusions

This study suggests that IBD may be a new risk factor for VD. The 8 hub genes may predict the IBD complicated with VD. Immune-related coDEGS may be related to their association, which requires further research to prove.

Deciphering peri-implantitis: Unraveling signature genes and immune cell associations through bioinformatics and machine learning

Early diagnosis of peri-implantitis (PI) is crucial to understand its pathological progression and prevention. This study is committed to investigating the signature genes, relevant signaling pathways and their associations with immune cells in PI. We analyzed differentially expressed genes (DEGs) from a PI dataset in the gene expression omnibus database. Functional enrichment analysis was conducted for these DEGs. Weighted Gene Co-expression Network Analysis was used to identify specific modules. Least absolute shrinkage and selection operator and support vector machine recursive feature elimination were ultimately applied to identify the signature genes. These genes were subsequently validated in an external dataset. And the immune cells infiltration was classified using CIBERSORT. A total of 180 DEGs were screened from GSE33774. Weighted Gene Co-expression Network Analysis revealed a significant association between the MEturquoise module and PI (cor = 0.6, P < .0001). Least absolute shrinkage and selection operator and support vector machine recursive feature elimination algorithms were applied to select the signature genes, containing myeloid-epithelial-reproductive tyrosine kinase, microfibrillar-associated protein 5, membrane-spanning 4A 4A, tribbles homolog 1. In the validation on the external dataset GSE106090, all these genes achieved area under curve values exceeding 0.95. GSEA analysis showed that these genes were correlated with the NOD-like receptor signaling pathway, metabolism of xenobiotics by cytochrome P450, and arachidonic acid metabolism. CIBERSORT revealed elevated levels of macrophage M2 and activated mast cells in PI. This study provides novel insights into understanding the molecular mechanisms of PI and contributes to advancements in its early diagnosis and prevention.

A 15-Inflammation-Related Gene Signature Predicts the Prognosis of Patients With Pancreatic Ductal Adenocarcinoma

Chronic inflammation promotes the development of pancreatic ductal adenocarcinoma (PDAC) and PDAC-related inflammatory tumor microenvironment facilitates tumor growth and metastasis. Thus, we aimed to study the association between inflammatory response and prognosis in patients with PDAC. We conducted the whole transcriptomic sequencing using tissue samples collected from patients diagnosed with PDAC (n = 106) recruited from Shandong Cancer Hospital. We first constructed a prognostic signature using 15 inflammation-related genes in The Cancer Genome Atlas (TCGA) cohort (n = 177) and further validated it in an independent International Cancer Genome Consortium (ICGC) cohort (n = 90) and our in-house cohort. PDAC patients with a higher risk score had poorer overall survival (OS) (P < 0.001; HR, 3.02; 95% CI, 1.94-4.70). The association between the prognostic signature and OS remained significant in the multivariable Cox regression adjusting for age, sex, alcohol exposure, diabetes, and stage (P < 0.001; HR, 2.91; 95% CI, 1.73-4.89). This gene signature also robustly predicted prognosis in the ICGC cohort (P = 0.01; HR, 1.94; 95% CI, 1.14-3.30) and our cohort (P < 0.001; HR, 2.40; 95% CI, 1.45-3.97). Immune subtype C3 (inflammatory) was enriched and CD8+ T cells were higher in patients with a lower risk score (P < 0.05). Furthermore, PDAC patients with higher risk scores were more sensitive to chemotherapy, immunotherapy, and PARP inhibitors (P < 0.05). In sum, we identified a novel gene signature that was associated with inflammatory response for risk stratification, prognosis prediction, and therapy guidance in PDAC patients. Future studies are warranted to validate the clinical utility of the signature.

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies

BACKGROUND

Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications.

METHODS

Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis.

RESULTS

Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs.

CONCLUSION

Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.

Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the "limma" package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.

Identification and validation of SLCO4C1 as a biological marker in hepatocellular carcinoma based on anoikis classification features

BACKGROUND

Hepatocellular carcinoma (HCC) exhibits a high degree of invasiveness and is closely associated with rapid disease progression. Multiple lines of evidence indicate a strong correlation between anoikis resistance and tumor progression, invasion, and metastasis. Nevertheless, the classification of anoikis in HCC and the investigation of novel biological target mechanisms in this context continue to pose challenges, requiring further exploration.

METHODS

Combined with HCC samples from TCGA, GEO and ICGC databases, cluster analysis was conducted on anoikis genes, revealing novel patterns among different subtypes. Significant gene analysis of different gene subtypes was performed using WCGNA. The anoikis prognostic risk model was established by Lasso-Cox. Go, KEGG, and GSEA were applied to investigate pathway enrichment primarily observed in risk groups. We compared the disparities in immune infiltration, TMB, tumor microenvironment (TME), and drug sensitivity between the two risk groups. RT-qPCR and Western blotting were performed to validate the expression levels of SLCO4C1 in HCC. The biological functions of SLCO4C1 in HCC cells were assessed through various experiments, including CCK8 assay, colony formation assay, invasion migration assay, wound healing assay, and flow cytometry analysis.

RESULTS

HCC was divided into 2 anoikis subtypes, and the subtypeB had a better prognosis. An anoikis prognostic model based on 12 (COPZ2, ACTG2, IFI27, SPP1, EPO, SLCO4C1, RAB26, STC2, RAC3, NQO1, MYCN, HSPA1B) risk genes is important for survival and prognosis. Significant differences were observed in immune cell infiltration, TME, and drug sensitivity analysis between the risk groups. SLCO4C1 was downregulated in HCC. SLCO4C1 downregulation promoted the proliferation, invasion, migration, and apoptosis of HCC cells. The tumor-suppressive role of SLCO4C1 in HCC has been confirmed.

CONCLUSIONS

Our study presents a novel anoikis classification method for HCC that reveals the association between anoikis features and HCC. The anoikis feature is a critical biomarker bridging tumor cell death and tumor immunity. In this study, we provided the first evidence of SLCO4C1 functioning as a tumor suppressor in HCC.

Development of a Novel Lipid Metabolism-related Gene Prognostic Signature for Patients with Colorectal Cancer

BACKGROUND

The purpose of this study was to explore the expression profiles of lipid metabolism-related genes in patients with Colorectal Cancer (CRC).

METHODS

The lipid metabolism statuses of CRC patients from The Cancer Genome Atlas (TCGA) were analyzed. Risk characteristics were constructed by univariate Cox regression and minimum Absolute contraction and Selection Operator (LASSO) Cox regression. A histogram was constructed based on factors such as age, sex, TNM stage, T stage, N stage, and risk score to provide a visual tool for clinicians to predict the probability of 1-year, 3-year, and 5-year OS for CRC patients. By determining Area Under Curve (AUC) values, the time-dependent Receiver Operating characteristic Curve (ROC) was used to evaluate the efficiency of our model in predicting prognosis.

RESULTS

A novel risk signal based on lipid metabolism-related genes was constructed to predict the survival of CRC patients. Risk characteristics were shown to be an independent prognostic factor in CRC patients (p <0.001). There were significant differences in the abundance and immune characteristics of tumor-filtering immune cells between high-risk and low-risk groups. The nomogram had a high potential for clinical application and the ROC AUC value was 0.827. Moreover, ROC analysis demonstrated that the nomogram model was more accurate to predict the survival of CRC patients than age, gender, stage and risk score.

CONCLUSION

In this study, we demonstrated a lipid metabolism-related genes prognosis biomarker associated with the tumor immune micro-environment in patients with CRC.

Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration

Severe Fever with thrombocytopenia syndrome (SFTS) is a highly fatal viral infectious disease that poses a significant threat to public health. Currently, the phase and pathogenesis of SFTS are not well understood, and there are no specific vaccines or effective treatment available. Therefore, it is crucial to identify biomarkers for diagnosing acute SFTS, which has a high mortality rate. In this study, we conducted differentially expressed genes (DEGs) analysis and WGCNA module analysis on the GSE144358 dataset, comparing the acute phase of SFTSV-infected patients with healthy individuals. Through the LASSO-Cox and random forest algorithms, a total of 2128 genes were analyzed, leading to the identification of four genes: ADIPOR1, CENPO, E2F2, and H2AC17. The GSEA analysis of these four genes demonstrated a significant correlation with immune cell function and cell cycle, aligning with the functional enrichment findings of DEGs. Furthermore, we also utilized CIBERSORT to analyze the immune cell infiltration and its correlation with characteristic genes. The results indicate that the combination of ADIPOR1, CENPO, E2F2, and H2AC17 genes has the potential as characteristic genes for diagnosing and studying the acute phase of SFTS virus (SFTSV) infection.

Bioinformatics analysis of potential common pathogenic mechanism for carotid atherosclerosis and Parkinson's disease

Background

Cerebrovascular disease (CVD) related to atherosclerosis and Parkinson's disease (PD) are two prevalent neurological disorders. They share common risk factors and frequently occur together. The aim of this study is to investigate the association between atherosclerosis and PD using genetic databases to gain a comprehensive understanding of underlying biological mechanisms.

Methods

The gene expression profiles of atherosclerosis (GSE28829 and GSE100927) and PD (GSE7621 and GSE49036) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for these two disorders, we constructed protein-protein interaction (PPI) networks and functional modules, and further identified hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The diagnostic effectiveness of these hub genes was evaluated using Receiver Operator Characteristic Curve (ROC) analysis. Furthermore, we used single sample gene set enrichment analysis (ssGSEA) to analyze immune cell infiltration and explored the association of the identified hub genes with infiltrating immune cells through Spearman's rank correlation analysis in R software.

Results

A total of 50 shared DEGs, with 36 up-regulated and 14 down-regulated genes, were identified through the intersection of DEGs of atherosclerosis and PD. Using LASSO regression, we identified six hub genes, namely C1QB, CD53, LY96, P2RX7, C3, and TNFSF13B, in the lambda.min model, and CD14, C1QB, CD53, P2RX7, C3, and TNFSF13B in the lambda.1se model. ROC analysis confirmed that both models had good diagnostic efficiency for atherosclerosis datasets GSE28829 (lambda.min AUC = 0.99, lambda.1se AUC = 0.986) and GSE100927 (lambda.min AUC = 0.922, lambda.1se AUC = 0.933), as well as for PD datasets GSE7621 (lambda.min AUC = 0.924, lambda.1se AUC = 0.944) and GSE49036 (lambda.min AUC = 0.894, lambda.1se AUC = 0.881). Furthermore, we found that activated B cells, effector memory CD8 + T cells, and macrophages were the shared correlated types of immune cells in both atherosclerosis and PD.

Conclusion

This study provided new sights into shared molecular mechanisms between these two disorders. These common hub genes and infiltrating immune cells offer promising clues for further experimental studies to explore the common pathogenesis of these disorders.

Corrigendum: Analysis of signature genes and association with immune cells infiltration in pediatric septic shock

[This corrects the article DOI: 10.3389/fimmu.2022.1056750.].

Identification of novel gene signatures and immune cell infiltration in intervertebral disc degeneration using bioinformatics analysis

Background: Intervertebral disc degeneration (IDD) is the leading cause of lower back pain, and an overall understanding of the molecular mechanisms related to IDD is still lacking. The purpose of this study was to explore gene signatures and immune cell infiltration related to IDD via bioinformatics analysis. Methods: A total of five expression profiles of mRNA and non-coding RNA were downloaded from the Gene Expression Omnibus (GEO) database. The potentially involved lncRNA/circRNA-miRNA-mRNA networks and protein-protein interaction networks were constructed by miRNet, circBank, STRING, and the Cytoscape database. Gene ontology, Kyoto Encyclopaedia of Genes and Genomes Analysis, Gene Set Enrichment Analysis, Gene Set Variation Analysis, Immune Infiltration Analysis, and Drug-Gene Interaction were used to analyse the top 20 hub genes. RT-qPCR was conducted to confirm the 12 differential expressions of genes both in the nucleus pulposus and annulus fibrosus tissues Results: There were 346 differentially expressed mRNAs, 12 differentially expressed miRNAs, 883 differentially expressed lncRNAs, and 916 differentially expressed circRNAs in the GEO database. Functional and enrichment analyses revealed hub genes associated with platelet activation, immune responses, focal adhesion, and PI3K-Akt signalling. The apoptotic pathway, the reactive oxygen species pathway, and oxidative phosphorylation play an essential role in IDD. Immune infiltration analysis demonstrated that the Treg cells had significant infiltration, and three levels of immune cells, including dendritic cells, Th2 cells, and tumour-infiltrating lymphocytes, were inhibited in IDD. Drug-gene interaction analysis showed that COL1A1 and COL1A2 were targeted by collagenase clostridium histolyticum, ocriplasmin, and PDGFRA was targeted by 66 drugs or molecular compounds. Finally, 24 cases of IDD tissues and 12 cases of normal disc tissues were collected, and the results of RT-qPCR were consistent with the bioinformatics results. Conclusion: Our data indicated that the 20 hub genes and immune cell infiltration were involved in the pathological process of IDD. In addition, the PDGFRA and two potential drugs were found to be significant in IDD development.

Comprehensive bioinformatics analysis reveals the crosstalk genes and immune relationship between the systemic lupus erythematosus and venous thromboembolism

Background

It is well known that patients with systemic lupus erythematosus (SLE) had a high risk of venous thromboembolism (VTE). This study aimed to identify the crosstalk genes between SLE and VTE and explored their clinical value and molecular mechanism initially.

Methods

We downloaded microarray datasets of SLE and VTE from the Gene Expression Omnibus (GEO) dataset. Differential expression analysis was applied to identify the crosstalk genes (CGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. The shared diagnostic biomarkers of the two diseases were further screened from CGs using least absolute shrinkage and selection operator (Lasso) regression. Two risk scores for SLE and VTE were constructed separately to predict the likelihood of illness according to the diagnostic biomarkers using a logical regression algorithm. The immune infiltration levels of SEL and VTE were estimated via the CIBERSORT algorithm and the relationship of CGs with immune cell infiltration was investigated. Finally, we explored potential phenotype subgroups in SLE and VTE based on the expression level of CGs through the consensus clustering method and studied immune cell infiltration in different subtypes.

Result

A total of 171 CGs were obtained by the intersection of differentially expressed genes (DEGs) between SLE and VTE cohorts. The functional enrichment shown these CGs were mainly related to immune pathways. After screening by lasso regression, we found that three hub CGs (RSAD2, HSP90AB1, and FPR2) were the optimal shared diagnostic biomarkers for SLE and VTE. Based on the expression level of RSAD2 and HSP90AB1, two risk prediction models for SLE and VTE were built by multifactor logistic regression and systemically validated in internal and external validation datasets. The immune infiltration results revealed that CGs were highly correlated with multiple infiltrated immunocytes. Consensus clustering was used to respectively regroup SLE and VTE patients into C1 and C2 clusters based on the CGs expression profile. The levels of immune cell infiltration and immune activation were higher in C1 than in C2 subtypes.

Conclusion

In our study, we further screen out diagnostic biomarkers from crosstalk genes SLE and VTE and built two risk scores. Our findings reveal a close relationship between CGs and the immune microenvironment of diseases. This provides clues for further exploring the common mechanism and interaction between the two diseases.

Exploring the potential common denominator pathogenesis of system lupus erythematosus with COVID-19 based on comprehensive bioinformatics analysis

Objective

Evidences show that there may be a link between SLE and COVID-19. The purpose of this study is to screen out the diagnostic biomarkers of systemic lupus erythematosus (SLE) with COVID-19 and explore the possible related mechanisms by the bioinformatics approach.

Methods

SLE and COVID-19 datasets were extracted separately from the NCBI Gene Expression Omnibus (GEO) database. The limma package in R was used to obtain the differential genes (DEGs). The protein interaction network information (PPI) and core functional modules were constructed in the STRING database using Cytoscape software. The hub genes were identified by the Cytohubba plugin, and TF-gene together with TF-miRNA regulatory networks were constructed via utilizing the Networkanalyst platform. Subsequently, we generated subject operating characteristic curves (ROC) to verify the diagnostic capabilities of these hub genes to predict the risk of SLE with COVID-19 infection. Finally, a single-sample gene set enrichment (ssGSEA) algorithm was used to analyze immune cell infiltration.

Results

A total of 6 common hub genes (CDC6, PLCG1, KIF15, LCK, CDC25C, and RASGRP1) were identified with high diagnostic validity. These gene functional enrichments were mainly involved in cell cycle, and inflammation-related pathways. Compared to the healthy controls, abnormal infiltration of immune cells was found in SLE and COVID-19, and the proportion of immune cells linked to the 6 hub genes.

Conclusion

Our research logically identified 6 candidate hub genes that could predict SLE complicated with COVID-19. This work provides a foothold for further study of potential pathogenesis in SLE and COVID-19.

Identification of mitophagy-related genes with potential clinical utility in myocardial infarction at transcriptional level

Background

Myocardial infarction (MI) ranks among the most prevalent cardiovascular diseases. Insufficient blood flow to the coronary arteries always leads to ischemic necrosis of the cardiac muscle. However, the mechanism of myocardial injury after MI remains unclear. This article aims to explore the potential common genes between mitophagy and MI and to construct a suitable prediction model.

Methods

Two Gene Expression Omnibus (GEO) datasets (GSE62646 and GSE59867) were used to screen the differential expression genes in peripheral blood. SVM, RF, and LASSO algorithm were employed to find MI and mitophagy-related genes. Moreover, DT, KNN, RF, SVM and LR were conducted to build the binary models, and screened the best model to further external validation (GSE61144) and internal validation (10-fold cross validation and Bootstrap), respectively. The performance of various machine learning models was compared. In addition, immune cell infiltration correlation analysis was conducted with MCP-Counter and CIBERSORT.

Results

We finally identified ATG5, TOMM20, MFN2 transcriptionally differed between MI and stable coronary artery diseases. Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively. Additionally, functional analysis suggested that monocytes and neutrophils might be involved in mitochondrial autophagy after myocardial infarction.

Conclusion

The data showed that the transcritional levels of ATG5, TOMM20 and MFN2 in patients with MI were significantly different from the control group, which might be helpful to further accurately diagnose diseases and have potential application value in clinical practice.

Exploring the common diagnostic gene KCNJ15 and shared pathway of ankylosing spondylitis and ulcerative colitis through integrated bioinformatics

Introduction: The similarity between ankylosing spondylitis (AS) and ulcerative colitis (UC) in incidence rate and pathogenesis has been revealed. But the common pathogenesis that explains the relationship between AS and UC is still lacked, and the related genetic research is limited. We purposed to explore shared biomarkers and pathways of AS and UC through integrated bioinformatics. Methods: Gene expression data of AS and UC were obtained in the GEO database. We applied weighted gene co-expression network analysis (WGCNA) to identify AS-related and UC-related co-expression gene modules. Subsequently, machine learning algorithm was used to further screen hub genes. We validated the expression level and diagnostic efficiency of the shared diagnostic gene of AS and UC in external datasets. Gene set enrichment analysis (GSEA) was applied to analyze pathway-level changes between disease group and normal group. Finally, we analyzed the relationship between hub biomarker and immune microenvironment by using the CIBERSORT deconvolution algorithm. Results: 203 genes were obtained by overlapping AS-related gene module and UC-related gene module. Through SVM-RFE algorithm, 19 hub diagnostic genes were selected for AS in GSE25101 and 6 hub diagnostic genes were selected for UC in GSE94648. KCNJ15 was obtained as a common diagnostic gene of AS and UC. The expression of KCNJ15 was validated in independent datasets, and the results showed that KCNJ15 were similarly upregulated in AS samples and UC samples. Besides, ROC analysis also revealed that KCNJ15 had good diagnostic efficacy. The GSEA analysis revealed that oxidative phosphorylation pathway was the shared pathway of AS and UC. In addition, CIBERSORT results revealed the correlation between KCNJ15 gene and immune microenvironment in AS and UC. Conclusion: We have explored a common diagnostic gene KCNJ15 and a shared oxidative phosphorylation pathway of AS and UC through integrated bioinformatics, which may provide a potential diagnostic biomarker and novel insight for studying the mechanism of AS-related UC.

Development and Validation of a Hypoxia-related Prognostic Model for Ovarian Cancer

BACKGROUND

The high heterogeneity of ovarian cancer (OC) brings great difficulties to its early diagnosis and prognostic forecast. There is an urgent need to establish a prognostic model of OC based on clinicopathological features and genomics.

METHODS

We identified hypoxia-related differentially expressed genes (DEGs) between OC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression (GTEx). LASSO Cox regression analysis was applied for building a prognostic model in the TCGA-GTEx cohorts, and its predictive value was validated in the GEO-OC cohort. Functional enrichment analysis was performed to investigate the underlying mechanisms. By constructing a hypoxia model of the SKOV3 cell line and applying qRT-PCR, we investigated the relationship between hypoxia with two novel genes in the prognostic model (ISG20 and ANGPTL4).

RESULTS

Twelve prognostic hypoxia-related DEGs were identified, and nine of them were selected to establish a prognostic model. OC patients were stratified into two risk groups, and the high-risk group showed reduced survival time compared to the low-risk group upon survival analysis. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent risk factor for overall survival. The biological function of the identified prognostic hypoxia-related gene signature was involved in immune cell infiltration. Low expression of ISG20 was observed in the CoCl₂-mimicked hypoxic SKOV3 cell line and negatively correlated with HIF-1α.

CONCLUSION

Our findings showed that this hypoxia-related gene signature could serve as a satisfactory prognostic classifier for OC and will be beneficial to the research and development of targeted therapeutic strategies.

Inherent Capability of Self-Assembling Nanostructures in Specific Proteasome Activation for Cancer Cell Pyroptosis

Understanding the direct interaction of nanostructures per se with biological systems is important for biomedical applications. However, whether nanostructures regulate biological systems by targeting specific cellular proteins remains largely unknown. In the present work, self-assembling nanomicelles are constructed using small-molecule oleanolic acid (OA) as a molecular template. Unexpectedly, without modifications by functional ligands, OA nanomicelles significantly activate cellular proteasome function by directly binding to 20S proteasome subunit alpha 6 (PSMA6). Mechanism study reveals that OA nanomicelles interact with PSMA6 to dynamically modulate its N-terminal domain conformation change, thereby controlling the entry of proteins into 20S proteasome. Subsequently, OA nanomicelles accelerate the degradation of several crucial proteins, thus potently driving cancer cell pyroptosis. For translational medicine, OA nanomicelles exhibit a significant anticancer potential in tumor-bearing mouse models and stimulate immune cell infiltration. Collectively, this proof-of-concept study advances the mechanical understanding of nanostructure-guided biological effects via their inherent capacity to activate proteasome.

Identification of mitochondria-related key gene and association with immune cells infiltration in intervertebral disc degeneration

Intervertebral disc (IVD) degeneration and its inflammatory microenvironment can result in discogenic pain, which has been shown to stem from the nucleus pulposus (NP). Increasing evidence suggests that mitochondrial related genes are strictly connected to cell functionality and, importantly, it can regulate cell immune activity in response to damaged associated signals. Therefore, identification of mitochondria related genes might offer new diagnostic markers and therapeutic targets for IVD degeneration. In this study, we identified key genes involved in NP tissue immune cell infiltration during IVD degeneration by bioinformatic analysis. The key modules were screened by weighted gene co-expression network analysis (WCGNA). Characteristic genes were identified by random forest analysis. Then gene set enrichment analysis (GSEA) was used to explore the signaling pathways associated with the signature genes. Subsequently, CIBERSORT was used to classify the infiltration of immune cells. Function of the hub gene was confirmed by PCR, Western blotting and ELISA. Finally, we identified MFN2 as a crucial molecule in the process of NP cell pyroptosis and NLRP3 inflammasome activation. We speculate that the increased MFN2 expression in NP tissue along with the infiltration of CD8⁺ T cells, NK cell and neutrophils play important roles in the pathogenesis of IVD degeneration.

CAV1 Impacts the Tumor Immune Microenvironment and Has Potential Value of Predicting Response to Immunotherapy in Esophageal Cancer

Caveolin-1 (CAV1) is one of the members of the caveolae, and the role of CAV1 in esophageal cancer (ESCA) is not completely clear. In this study, we found that expression of CAV1 was downregulated in ESCA in The Cancer Genome Atlas and the Genotype-Tissue Expression (GTEx) database and we also use immunohistochemistry of tissue microarray for verification. Then, we used bioinformatics methods to investigate the prognostic value of CAV1, influence on immune cell infiltration in tumor microenvironment (TME) and responding to immunotherapy in ESCA. Our result indicated that CAV1 designs an inflamed TME in ESCA based on the evidence that CAV1 positively correlated with immunomodulators, immune score, stomal score, cancer immunity cycles, tumor-infiltrating immune cells, T cell inflamed score, and immune checkpoints. Immunophenoscore, Tumor Immune Dysfunction and Exclusion algorithms, and the mutation analysis show that the downregulated CAV1 expression indicated higher tumor mutation burden and higher rate of response to immune checkpoint inhibitors (ICIs) in the low-expression group. In a word, our study demonstrated the impact of CAV1 to the TME in ESCA and it may be a new target for ESCA immunotherapy. In addition, the expression of CAV1 can predict the clinical response to ICIs, which may provide clinical treatment guidance.

Identification of Immune Infiltration and the Potential Biomarkers in Diabetic Peripheral Neuropathy through Bioinformatics and Machine Learning Methods

Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications in diabetes. Previous studies have shown that chronic neuroinflammation was associated with DPN. However, further research is needed to investigate the exact immune molecular mechanism underlying the pathogenesis of DPN. Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by R software. After functional enrichment analysis of DEGs, a protein-protein interaction (PPI) network analysis was performed. The CIBERSORT algorithm was used to evaluate the infiltration of immune cells in DPN. Next, the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential DPN diagnostic markers. Finally, the results were further validated by qRT-PCR. A total of 1308 DEGs were screened in this study. Enrichment analysis identified that DEGs were significantly enriched in immune-related biological functions and pathways. Immune cell infiltration analysis found that M1 and M2 macrophages, monocytes, resting mast cells, resting CD4 memory T cells and follicular helper T cells were involved in the development of DPN. LTBP2 and GPNMB were identified as diagnostic markers of DPN. qRT-PCR results showed that 15 mRNAs, including LTBP2 and GPNMB, were differentially expressed, consistent with the microarray results. In conclusion, LTBP2 and GPNMB can be used as novel candidate molecular diagnostic markers for DPN. Furthermore, the infiltration of immune cells plays an important role in the progression of DPN.

Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs

Background

Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD.

Methods

The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the "ConsensusClusterPlus" package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy.

Results

Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group.

Conclusions

Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis.

Analysis of signature genes and association with immune cells infiltration in pediatric septic shock

Background

Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock.

Methods

We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT.

Results

A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p<0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration.

Conclusion

UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration.

Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

Background

Androgenetic alopecia (AGA) is a type of non-scarring hair loss. Current drugs for AGA are accompanied by adverse reactions and a high recurrence rate. Thus, the discovery of diagnostic biomarkers and therapeutic targets for AGA remains imperatively warranted.

Methods

The GSE90594 dataset, which contained scalp skin biopsies from 14 male AGA cases and healthy volunteers, was used to identify the differentially expressed genes (DEGs). Functional enrichment analysis was subsequently performed. Next, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database combined with the cytoHubba plugin of Cytoscape were used to obtain the key genes of AGA. Thereafter, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was performed to evaluate the relative abundance of immune cells between male AGA patients and healthy controls. The correlation between key genes and infiltrating immune cells was analyzed to obtain the significant immune-cell related genes (IRGs), then intersected with the DEGs between immortalized balding and non-balding human dermal papilla cells (DPCs) of the GSE93766 dataset as well as the DEGs obtained by the GSE90594 dataset, thus obtaining the hub genes of AGA. Finally, the hub genes were validated using GSE36169, which contained expression profiling of tissues biopsied from haired and bald scalps of five individuals with AGA.

Results

A total of 234 DEGs were obtained from the GSE90594 dataset, which were mainly enriched in the extracellular matrix (ECM)-related pathways and immune-related activities. The STRING database and ten algorithms in the cytoHubba plugin of Cytoscape disclosed 21 key DEGs. The results of the CIBERSORT algorithm revealed the relative abundances of 20 kinds of immune cells between diseased and healthy individuals, and yielded 15 IRGs involved in the pathogenesis of AGA. Next, the intersection analysis identified four hub genes of AGA, comprising COL1A2, PCOLCE, ITGAX, and LOX. The GSE36169 dataset validated the expression pattern of hub genes in the haired scalp of AGA patients.

Conclusions

We discovered that the hub genes identified are closely linked with the causative factors of AGA, which could be used as the viable diagnostic and therapeutic target in the clinical applications.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

Construction of three-gene-based prognostic signature and analysis of immune cells infiltration in children and young adults with B-acute lymphoblastic leukemia

Background

Although B‐acute lymphoblastic leukemia (B‐ALL) patients' survival has been improved dramatically, some cases still relapse. This study aimed to explore the prognosis‐related novel differentially expressed genes (DEGs) for predicting the overall survival (OS) of children and young adults (CAYAs) with B‐ALL and analyze the immune‐related factors contributing to poor prognosis.

Methods

GSE48558 and GSE79533 from Gene Expression Omnibus (GEO) and clinical sample information and mRNA‐seq from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were retrieved. Prognosis‐related key genes were enrolled to build a Cox proportional model using multivariate Cox regression. Five‐year OS of patients, clinical characteristic relevance and clinical independence were assessed based on the model. The mRNA levels of prognosis‐related genes were validated in our samples and the difference of immune cells composition between high‐risk and low‐risk patients were compared.

Results

One hundred and twelve DEGs between normal B cells and B‐ALL cells were identified based on GSE datasets. They were mainly participated in protein binding and HIF‐1 signaling pathway. One hundred and eighty‐nine clinical samples were enrolled in the study, both Kaplan–Meier (KM) analysis and univariate Cox regression analysis showed that CYBB, BCL2A1, IFI30, and EFNB1 were associated with prognosis, CYBB, BCL2A1, and EFNB1 were used to construct prognostic risk model. Moreover, compared to clinical indicators, the three‐gene signature was an independent prognostic factor for CAYAs with B‐ALL. Finally, the mRNA levels of CYBB, BCL2A1, and EFNB1 were significantly lower in B‐ALL group as compared to controls. The high‐risk group had a significantly higher percentage of infiltrated immune cells.

Conclusion

We constructed a novel three‐gene signature with independent prognostic factor for predicting 5‐year OS of CAYAs with B‐ALL. Additionally, we discovered the difference of immune cells composition between high‐risk and low‐risk groups. This study may help to customize individual treatment and improve prognosis of CAYAs with B‐ALL.

Sirtuin 7 Deficiency Reduces Inflammation and Tubular Damage Induced by an Episode of Acute Kidney Injury

Acute kidney injury (AKI) is a public health problem worldwide. Sirtuins are a family of seven NAD+-dependent deacylases, Overexpression of Sirtuin 1, 3, and 5 protect against AKI. However, the role of Sirtuin 7 (Sirt7) in AKI is not known. Here, we analyzed how Sirt7 deficient mice (KO-Sirt7) were affected by AKI. As expected, wild-type and Sirt7 heterozygotes mice that underwent renal ischemia/reperfusion (IR) exhibited the characteristic hallmarks of AKI: renal dysfunction, tubular damage, albuminuria, increased oxidative stress, and renal inflammation. In contrast, the KO-Sirt7+IR mice were protected from AKI, exhibiting lesser albuminuria and reduction in urinary biomarkers of tubular damage, despite similar renal dysfunction. The renoprotection in the Sirt7-KO+IR group was associated with reduced kidney weight, minor expression of inflammatory cytokines and less renal infiltration of inflammatory cells. This anti-inflammatory effect was related to diminished p65 expression and in its active phosphorylation, as well as by a reduction in p65 nuclear translocation. Sirt7 deficient mice are protected from AKI, suggesting that this histone deacetylase promotes tubular damage and renal inflammation. Therefore, our findings indicate that Sirt7 inhibitors may be an attractive therapeutic target to reduce NFκB signaling.

Identification of Immune-Related Key Genes in Ovarian Cancer Based on WGCNA

Background: Ovarian cancer (OV) is a fatal gynecologic malignancy and has poor survival rate in women over the age of forty. In our study, we aimed to identify genes related to immune microenvironment regulations and explore genes associated with OV prognosis. Methods: The RNA-seq data of GDC TCGA Ovarian Cancer cohort of 376 patients was retrieved from website. Weighted gene co-expression network analysis (WGCNA) and ESTIMATE algorithm were applied to identify the key genes associated with the immune scores. The correlation between key genes and 22 immune cell types were estimated by using CIBERSORT algorithms. Results: WGCNA showed that the pink module was most correlated with the immune score. Seven of 14 key genes (FCRL3, IFNG, KCNA3, LY9, PLA2G2D, THEMIS, and TRAT1) were significantly associated with the OS of OV patients. Correlation analysis showed our key genes positively related to M1 macrophages, CD8 T cells, plasma cells, regulatory T (Treg) cells and activated memory CD4 T cells, and negatively related to naive CD4 T cells, M0 macrophages, activated dendritic cells (DCs) and memory B cells. Kaplan-Meier survival analysis showed that lower abundances of neutrophils and higher abundances of M1 macrophages, plasma cells, T cells gamma delta (γδT) cells and follicular helper T (Tfh) cells predicted better OV prognosis. Conclusion: Forteen key genes related to the immune infiltrating of OV were identified, and seven of them were significantly related to prognosis. These key genes have potential roles in tumor infiltrating immune cells differentiation and proliferation. This study provided potential prognostic markers and immunotherapy targets for OV.

Decreased interferon regulatory factor 6 expression due to DNA hypermethylation predicts an unfavorable prognosis in clear cell renal cell carcinoma

Background: Emerging evidences have indicated that IRF6, as a member of the Interferon regulatory factors (IRFs) family, plays important roles in a variety of tumors. However, the expression status of IRF6 and its prognostic value in clear cell renal cell carcinoma (ccRCC) remain unclear. Methods: In this study, we used TCGA-KIRC, GEO and TIP databases and immunohistochemistry staining to determine the expression profile, clinico-pathological features and prognostic value of IRF6 in ccRCC. MSP and demethylation analysis were utilized to verify the regulatory effect of DNA methylation on IRF6 expression. Results: Our results found that IRF6 expression was downregulated in ccRCC tissues and cell lines, and decreased IRF6 expression was associated with worse clinicopathological features and poorer prognosis. Besides, the results of multivariate Cox regression analysis also confirmed that decreased IRF6 expression was an independently risk factor predictor of shorter Overall Survival (OS) (HR: 0.8524, 95%CI: 0.7614-0.9543, P=0.0056) and Disease Free Survival (DFS) (HR: 0.7024, 95%CI: 0.6087-0.8104, P<0.0001) in ccRCC patients. Moreover, the results of MSP and demethylation analysis validated that decreased IRF6 expression was caused by DNA hypermethylation. Furthermore, our results showed that IRF6 expression was associated with the infiltration levels of multiple immune cells in ccRCC. Conclusions: These findings demonstrated that IRF6 expression was significantly reduced in ccRCC and DNA hypermethylation played an important role in decreased IRF6 expression. In addition, the decrease of IRF6 was related to the unfavorable prognosis of ccRCC patients and the alterations of tumor immune cells infiltration.

Identification and Validation of Hypoxia-Related lncRNA Signature as a Prognostic Model for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Hypoxia is a critical clinical characteristic and acts as a significant part in the development and cancers’ prognosis. The prognostic value and biological functions of hypoxia-related lncRNAs in hepatocellular carcinoma is little known. Thus, we aim to establish a hypoxia-related lncRNA signature to predict the HCC patients’ survival. First, we extracted the hypoxia-related genes and expression of lncRNAs from the MSigDB and TCGA database, respectively. The co-expression analysis among hypoxia-related mRNAs and lncRNAs was employed to identify hypoxia-related lncRNAs. Then, comprehensive analyses of lncRNAs expression level and survival data were applied to establish the signature. We built a prognostic signature on the foundation of the three differently expressed hypoxia-related lncRNAs. Kaplan-Meier curves indicated the low-risk group is associated with better survival. The 1−, 3−, and 5 years AUC values of the signature were 0.805, 0.672 and 0.63 respectively. The test set performed consistent outcomes. A nomogram was built grounded on the risk score and clinicopathological features. GSEA showed the immune-related pathways in high-risk group, while metabolism-related pathways in low-risk group. Besides, we found this model was correlated with the clinical features, tumor immune cell infiltration, immune checkpoints, and m6A-related genes. Finally, a novel signature based on hypoxia-related lncRNAs was established and validated for predicting HCC patients’ survival and may offer some useful information for immunotherapies.

Heat Shock Protein Beta 1 is a Prognostic Biomarker and Correlated with Immune Infiltrates in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most serious malignancies. The main features of HCC are vascular invasion and drug resistance. Ferroptosis is a novel cell program that is involved in several diseases, such as cancer. Heat shock protein beta 1 (HSPB1) is a major component of heat shock proteins. A recent study showed that HSPB1 could be a new therapeutic target for colorectal cancer with 5-fluorouracil-acquired resistance. However, the functional role of HSPB1 in HCC remains unclear.

Aim

The aim of this study is to clarify HSPB1 expression in HCC and its potential therapeutic and prognostic value.

Methods

We collected data on HSPB1 expression levels in HCC and normal liver tissues from The Cancer Genome Atlas and Gene Expression Omnibus databases. We then validated it using immunohistochemistry (IHC). Receiver operating characteristic and Kaplan–Meier survival curves were used to investigate the role of HSPB1 in the prognosis analysis of HCC. Further, we used the online Search Tool for the Retrieval of Interacting Genes/Proteins website, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes to conduct enrichment analysis and identify the predictive signaling pathways. Meanwhile, we used the TIMER and GSVA package of R (v3.6.3) to analyze the association between HSPB1 and immunocyte infiltration.

Results

Compared to normal tissues, there was differential expression of HSPB1 in pan-cancers. HSPB1 expression was higher in HCC tissues than in normal tissues (p<0.05). There was an evident significant difference between HSPB1 mRNA levels and histologic grade, vascular invasion, and alpha-fetoprotein level (all p values<0.05). Univariate analysis indicated that HCC patients with high HSPB1 levels had shorter overall survival rates than those with low HSPB1 levels (p<0.05). MAPK14, HSPA8, MAPKAPK3, MAPKAPK5, and MAPKAPK2 are essential proteins that interact with HSPB1. There was a significant correlation between HSPB1 expression levels and immune cell infiltration, including CD4⁺ T cells (r=0.203, p<0.05).

Conclusion

High HSPB1 expression is closely associated with a worse prognosis in HCC patients, and HSPB1 may be a target of immunotherapy in HCC.

Comprehensive analysis of alternative splicing profiling reveals novel events associated with prognosis and the infiltration of immune cells in prostate cancer

Background

Alternative splicing (AS) is believed to play a vital role in tumor development. Therefore, comprehensive investigation of AS and its biological function in prostate cancer (PCa) is crucial.

Methods

The AS profiling of 489 patients with PCa was obtained from The Cancer Genome Atlas (TCGA) SpliceSeq database. Bioinformatics tools were used to describe splicing associations and build prognostic models. Unsupervised clustering of the determined prognostic AS events and the relationship with immune characteristics were also explored.

Results

In total, 20,723 AS events were detected and 2,805 were identified in PCa. In the regulatory networks, the data suggested a significant correlation between splicing factor (SF) expression and AS events. To stratify the progression risk of PCa patients, prognostic models were constructed using splicing patterns. Six AS events were screened out as independent prognostic factors for progression-free survival. Based on the gene features, we constructed the combined prognostic predictors model, and the receiver operating characteristic (ROC) curve for this model reached a high area under the ROC curve (AUC) of 0.729793, indicating a favorable ability to predict patient outcomes. Through unsupervised clustering analysis, the correlations between AS-based clusters and prognosis as well as immune characteristics were revealed. The correlation analysis on TIMER revealed the relationship between gene expression and immune cell infiltration.

Conclusions

This in-depth genome-wide analysis of the AS profiling in PCa revealed unique AS events associated with cancer progression and the infiltration of immune cells, with potential for predicting outcomes and therapeutic responses.

Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.

Methods

Five microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by "Batch correction" and "RobustRankAggreg" methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman's rank correlation analysis in R software.

Results

Ten significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package "UpSet". CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.

Conclusions

CCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.

Identification of a Gene Set Correlated With Immune Status in Ovarian Cancer by Transcriptome-Wide Data Mining

Immune checkpoint blocking (ICB) immunotherapy has achieved great success in the treatment of various malignancies. Although not have been approved for the treatment of ovarian cancer (OC), it has been actively tested for the treatment of OC. However, biomarkers that could indicate the immune status of OC and predict the response to ICB are rare. We downloaded RNAseq and clinical data of OC from The Cancer Genome Atlas (TCGA). Data analysis revealed both TMB^high and immunity^high were significantly related to better survival of OC. Up-regulated differentially expressed genes (Up-DEGs) were identified by analyzing the gene expression levels. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the "GSVA" and "limma" package in R software. The correlation of genes with overall survival was also analyzed by conducted Kaplan-Meier survival analysis. Four genes, CXCL13, FCRLA, MS4A1, and PLA2G2D were found positively correlated with better prognosis of OC and mainly involved in immune response-related pathways. Finally, TIMER and TIDE were used to predict gene immune function and its association with immunotherapy. We found that these four genes were positively correlated with better response to immune checkpoint blockade-based immunotherapy. Altogether, CXCL13, FCRLA, MS4A1, and PLA2G2D may be used as potential therapeutic genes for reflecting OC immune status and predicting response to immunotherapy.

m6A RNA methylation regulators were associated with the malignancy and prognosis of ovarian cancer

N6-methyladenosine (m6A) RNA methylation regulators play a regulatory role in tumor pathogenesis and development. However, the role of m6A regulator genes in ovarian cancer (OC) has not been fully elucidated. This study aims to investigate the mRNA expressions, clinicopathological features, and prognostic values of m6A regulators in OC. Here, we demonstrate that the 17 m6A RNA methylation regulators are differentially expressed in ovarian cancer and normal tissues. By using consensus clustering, all ovarian cancer patients can be divided into two subgroups (cluster 1 and 2) based on the expression of 17 m6A RNA methylation regulators. Using Gene Set Enrichment Analysis, we identified that cluster 1 was most connected to oxidative phosphorylation pathways. Regression models identified that prognosis is associated with HNRNPA2B1, KIAA1429, and WTAP. qRT-PCR result show that the expression trends of HNRNPA2B1 and KIAA1429 are consistent with the predicted results. Multivariate Cox regression analysis results show that the risk score was an independent predictive factor in OV. The overall survival of high-risk patients was significantly shorter than that of low-risk patients. ROC curve analysis showed that the prognostic signature precisely predicted the 5-year survival of OV patients. A nomogram was developed to predict each patient's survival probability and well calibrated and showed a satisfactory discrimination. Dendritic fraction, macrophage fraction, and neutrophil fraction showed higher fraction in high-risk patients. In conclusion, m6A RNA methylation regulators are vital participants in ovarian cancer pathology, and three-gene mRNA levels are valuable factors for prognosis predictions.

Exploration of a Robust and Prognostic Immune Related Gene Signature for Cervical Squamous Cell Carcinoma

Background: Cervical squamous cell carcinoma (CESC) is one of the most frequent malignancies in women worldwide. The level of immune cell infiltration and immune-related genes (IRGs) can significantly affect the prognosis and immunotherapy of CESC patients. Thus, this study aimed to identify an immune-related prognostic signature for CESC.

Methods: TCGA-CESC cohorts, obtained from TCGA database, were divided into the training group and testing group; while GSE44001 dataset from GEO database was viewed as external validation group. ESTIMATE algorithm was applied to evaluate the infiltration levels of immune cells of CESC patients. IRGs were screened out through weighted gene co-expression network analysis (WGCNA). A multi-gene prognostic signature based on IRGs was constructed using LASSO penalized Cox proportional hazards regression, which was validated through Kaplan–Meier, Cox, and receiver operating characteristic curve (ROC) analyses. The abundance of immune cells was calculated using ssGSEA algorithm in the ImmuCellAI database, and the response to immunotherapy was evaluated using immunophenoscore (IPS) analysis and the TIDE algorithm.

Results: In TCGA-CESC cohorts, higher levels of immune cell infiltration were closely associated with better prognoses. Moreover, a prognostic signature was constructed using three IRGs. Based on this given signature, Kaplan–Meier analysis suggested the significant differences in overall survival (OS) and the ROC analysis demonstrated its robust predictive potential for CESC prognosis, further confirmed by internal and external validation. Additionally, multivariate Cox analysis revealed that the three IRGs signature served as an independent prognostic factor for CESC. In the three-IRGs signature low-risk group, the infiltrating immune cells (B cells, CD4/8 + T cells, cytotoxic T cells, macrophages and so on) were much more abundant than that in high-risk group. Ultimately, IPS and TIDE analyses showed that low-risk CESC patients appeared to present with a better response to immunotherapy and a better prognosis than high-risk patients.

Conclusion: The present prognostic signature based on three IRGs (CD3E, CD3D, LCK) was not only reliable for survival prediction but efficient to predict the clinical response to immunotherapy for CESC patients, which might assist in guiding more precise individual treatment in the future.

SYNE1 mutation may enhance the response to immune checkpoint blockade therapy in clear cell renal cell carcinoma patients

As one of the 10 most common cancers in men, the incidence of renal cell carcinoma (RCC) has been increasing in recent years. Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of RCC, counting for 80%-90% of cases. Immunotherapy is becoming increasingly important in the treatment of advanced RCC. Tumor mutation burden (TMB) is a potent marker for predicting the response to immune checkpoint blockade (ICB) treatment. Here, we analyzed somatic mutation data for ccRCC from The Cancer Genome Atlas datasets. We found that the frequently mutated gene SYNE1 is associated with higher TMBs and with a poor clinical prognosis. To further investigate the relationship between SYNE1 mutation and the immune system, we used Gene Set Enrichment Analysis and the CIBERSORT algorithm. They showed that SYNE1 mutations correlate with immune system pathways and immune cell tumor infiltration. We also found that SYNE1 mutation correlated with a better response to ICB therapy. Thus, mutation of SYNE1 correlates with a higher TMB and a poorer outcome in ccRCC, but may mediate better responses to ICB therapy.

Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment.

Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT.

Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352).

Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.

Identification of an Immune-Related Prognostic Signature Associated With Immune Infiltration in Melanoma

Melanoma is the leading cause of cancer-related death among skin tumors, with an increasing incidence worldwide. Few studies have effectively investigated the significance of an immune-related gene (IRG) signature for melanoma prognosis. Here, we constructed an IRGs prognostic signature using bioinformatics methods and evaluated and validated its predictive capability. Then, immune cell infiltration and tumor mutation burden (TMB) landscapes associated with this signature in melanoma were analyzed comprehensively. With the 10-IRG prognostic signature, melanoma patients in the low-risk group showed better survival with distinct features of high immune cell infiltration and TMB. Importantly, melanoma patients in this subgroup were significantly responsive to MAGE-A3 in the validation cohort. This immune-related prognostic signature is thus a reliable tool to predict melanoma prognosis; as the underlying mechanism of this signature is associated with immune infiltration and mutation burden, it might reflect the benefit of immunotherapy to patients.

TERT mutations correlate with higher TMB value and unique tumor microenvironment and may be a potential biomarker for anti-CTLA4 treatment

Immune checkpoint inhibitors (ICIs) have recently changed therapeutic paradigms for patients across multiple cancer types. However, current biomarkers cannot accurately predict responses to ICIs. Telomerase reverse transcriptase (TERT) mutations lead to an aberrant upregulation of TERT expression, and ultimately allow telomere maintenance, thus supporting immortalization of cancer cells. This study aimed to investigate whether the TERT mutation is a potential predictor of ICI treatment across all cancer types. TERT mutations positively correlated with a higher tumor mutational burden (TMB) value, neoantigen load, and tumor purity. Lymphocyte infiltration, macrophage regulation, interferon‐gamma (IFN‐γ) response, and transforming growth factor‐β (TGF‐β) response which was representative immune‐expression signatures, all had higher signature scores in the TERT mutation group. Activated CD4 T cell, naïve B cell, activated dendritic cell, M0 macrophage, M1 macrophage, neutrophil, resting NK cell, and plasma cells all had relatively higher immune scores in the TERT mutation group, whereas Th series cells, memory B cell, resting mast cells, monocytes, and activated NK cells had lower immune scores. Notably, in the subgroup analysis of monotherapy and combination ICI treatment, only in the anti‐cytotoxic‐T‐lymphocyte‐associated antigen 4 (anti‐CTLA4) group, patients with TERT mutations had a better prognosis, especially for melanoma. Therefore, TERT mutations were closely related to a higher TMB value and unique tumor microenvironment, which may be the reason that TERT mutations may be a potential biomarker for anti‐CTLA4 treatment.

Construction of immune-related and prognostic lncRNA clusters and identification of their immune and genomic alterations characteristics in lung adenocarcinoma samples

Long non-coding RNAs (lncRNAs) play an important role in various biological processes of lung adenocarcinoma (LUAD), such as immune response regulation, tumor microenvironment remodeling and genomic alteration. Nevertheless, immune-related lncRNAs and their immune and genomic alterations characteristics in LUAD samples still remain unreported. Here, using various public databases, statistic and software tools, we constructed two immune-related lncRNA clusters with different immune and genomic alterations characteristics. Notably, cluster 1 had a stronger immunosuppressive tumor microenvironment (TME) and a higher mutation frequency than cluster 2, especially the mutant genes, such as Kelch-like ECH-associated protein 1 (KEAP1) and toll like receptor 4 (TLR4). In cluster 1, both the amplified and deleted portions of copy number variation (CNV) segments were enriched and cyclin dependent kinase inhibitor 2A (CDKN2A) was significantly deleted. GSVA analysis revealed that these immune-related lncRNAs may be involved in stem cell and EMT functions. Furthermore, cluster 1 was related to worse prognosis of LUAD patients. Therefore, we constructed two immune-related and prognostic lncRNA clusters and identified their immune and genomic alterations characteristics in LUAD samples, which could well divide LUAD patients into different immune phenotypes and help to understand immune molecular mechanisms of LUAD.

Identification of miRNA-mRNA Regulatory Network and Construction of Prognostic Signature in Cervical Cancer

Cervical cancer (CC) remains a most prevalent female cancer worldwide, but there are few biomarkers used in diagnosis and prognosis of CC. The aim of this study is to find reliable and effective biomarkers regarding CC development. Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to search potential miRNA-mRNA in CC. The gene ontology term enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were conducted to reveal the underlying functions and pathways of differently expressed genes (DEGs). Univariate Cox, multivariate Cox, and risk scoring methods were performed to identify a prognostic model. A total of 209 DEGs of CC were identified. In the protein-protein interaction network, hub module, and hub genes were recognized. Based on DEGs, three small molecules (thioguanosine, apigenin, and trichostatin A) were screened out as potential drugs. Two miRNAs (hsa-mir-101-3p and hsa-mir-6507-5p) and some transcription factors were found to be associated with prognosis of CC. A five-candidate gene signature (APOBEC3B, DSG2, CXCL8, ABCA8, and PLAGL1) was constructed to stratify risk subgroups for patients with CC. The risk score of the prognostic model was also found to be associated with immune cells infiltration, including mast cell activation, natural killer cells resting, dendritic cells resting, T cells regulatory (Tregs), and T cells follicular helper. The miRNA-mRNA regulatory network and the prognostic model are of great clinical significance in promoting prognosis prediction and treatment of CC.

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Lower-grade gliomas (LrGG), characterized by invasiveness and heterogeneity, remain incurable with current therapies. Clinicopathological features provide insufficient information to guide optimal individual treatment and cannot predict prognosis completely. Recently, an increasing amount of studies indicate that the tumor microenvironment plays a pivotal role in tumor malignancy and treatment responses. However, studies focusing on the tumor microenvironment (TME) of LrGG are still limited. In this study, taking advantage of the currently popular computational methods for estimating the infiltration of tumor-associated normal cells in tumor samples and using weighted gene co-expression network analysis, we screened the co-expressed gene modules associated with the TME and further identified the prognostic hub genes in these modules. Furthermore, eight prognostic hub genes (ARHGDIB, CLIC1, OAS3, PDIA4, PARP9, STAT1, TAP2, and TAGLN2) were selected to construct a prognostic risk score model using the least absolute shrinkage and selection operator method. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor for LrGG. Moreover, time-dependent ROC curves indicated that our model had favorable efficiency in predicting both short- and long-term prognosis in LrGG patients, and the stratified survival analysis demonstrated that our model had prognostic value for different subgroups of LrGG patients. Additionally, our model had potential value for predicting the sensitivity of LrGG patients to radio- and chemotherapy. Besides, differential expression analysis showed that the eight genes were aberrantly expressed in LrGG compared to normal brain tissue. Correlation analysis revealed that the expression of the eight genes was significantly associated with the infiltration levels of six types of immune cells in LrGG. In summary, the TME-related eight-gene signature was significantly associated with the prognosis of LrGG patients. They might act as potential prognostic biomarkers for LrGG patients, offer better stratification for future clinical trials, and be candidate targets for immunotherapy, thus deserving further investigation.

Low dose radiation primed iNOS + M1macrophages modulate angiogenic programming of tumor derived endothelium

Solid tumors are covered by stroma, which is hypoxic in nature and composed of various non-malignant components such as endothelial cells, fibroblasts, and pericytes that support tumor growth. Tumor stroma represents a mechanical barrier for tumor infiltration of CD8+ effector T cells in particular. In this context, our previous studies have demonstrated the therapeutic impact of Low-Dose Radiation (LDR)-primed and M1-retuned (iNOS+) peritumoral macrophages that produce inducible nitric oxide, have immunological roles on tumor infiltration of effector T cells, cancer-related inflammation, and subsequent tumor immune rejection in a mouse model of pancreatic cancer. These findings suggested a possible modification of tumor endothelium by LDR-primed macrophages. In line with these observations, here we demonstrate the influence of LDR in down-modulating HIF-1 in irradiated tumors in the course of polarization of irradiated tumor-associated macrophages toward an M1 phenotype. Furthermore, we demonstrate that M1 macrophages which are primed by LDR can directly influence angiogenic responses in eNOS+ endothelial cells which produce nitric oxide having both vascular and physiological roles. Furthermore, we demonstrate that naïve macrophages, upon differentiating to an M1 phenotype either by Th1 stimuli or LDR, potentially modify sphingosine-1-phosphate/VEGF-induced angiogenic signaling in tumor-derived endothelial cells with tumorigenic potential, thus indicating the significance of iNOS+ macrophages in modulating signaling in eNOS+ tumor-derived endothelium. Our study suggests that iNOS+ macrophages can activate tumor endothelium which may contribute to cancer-directed immunotherapy in particular.