Nomogram Based on microRNA Signature Contributes to Improve Survival Prediction of Clear Cell Renal Cell Carcinoma

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.

Identification and validation of a novel tumor driver gene signature for diagnosis and prognosis of head and neck squamous cell carcinoma

Objective: Head and neck squamous cell carcinoma (HNSCC) is a common heterogeneous cancer with complex carcinogenic factors. However, the current TNM staging criteria to judge its severity to formulate treatment plans and evaluate the prognosis are particularly weak. Therefore, a robust diagnostic model capable of accurately diagnosing and predicting HNSCC should be established. Methods: Gene expression and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Key prognostic genes associated with HNSCC were screened with the weighted gene co-expression network analysis and least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. We used the timeROC and survival R packages to conduct time-dependent receiver operating characteristic curve analyses and calculated the area under the curve at different time points of model prediction. Patients in the training and validation groups were divided into high- and low-risk subgroups, and Kaplan-Meier (K-M) survival curves were plotted for all subgroups. Subsequently, LASSO and support vector machine algorithms were used to screen genes to construct diagnostic model. Furthermore, we used the Wilcoxon signed-rank test to compare the half-maximal inhibitory concentrations of common chemotherapy drugs among patients in different risk groups. Finally, the expression levels of eight genes were measured using quantitative real-time polymerase chain reaction and immunohistochemistry. Results: Ten genes (SSB, PFKP, NAT10, PCDH9, SHANK2, PAX8, CELSR3, DCLRE1C, MAP2K7, and ODF4) with prognostic potential were identified, and a risk score was derived accordingly. Patients were divided into high- and low-risk groups based on the median risk score. The K-M survival curves confirmed that patients with high scores had significantly worse overall survival. Receiver operating characteristic curves proved that the prognostic signature had good sensitivity and specificity for predicting the prognosis of patients with HNSCC. Univariate and multivariate Cox regression analyses confirmed that the gene signature was an independent prognostic risk factor for HNSCC. Diagnostic model was built by identifying eight genes (SSB, PFKP, NAT10, PCDH9, CELSR3, DCLRE1C, MAP2K7, and ODF4). The high-risk group showed higher sensitivity to various common chemotherapeutic drugs. DCLRE1C expression was higher in normal tissues than in HNSCC tissues. Conclusion: Our study identified the important role of tumor-driver genes in HNSCC and their potential clinical diagnostic and prognostic values to facilitate individualized management of patients with HNSCC.

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.

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.

Predicting Diagnostic Gene Biomarkers Associated With Immune Checkpoints, N6-Methyladenosine, and Ferroptosis in Patients With Acute Myocardial Infarction

This study aimed to determine early diagnosis genes of acute myocardial infarction (AMI) and then validate their association with ferroptosis, immune checkpoints, and N6-methyladenosine (m6A), which may provide a potential method for the early diagnosis of AMI. Firstly, we downloaded microarray data from NCBI (GSE61144, GSE60993, and GSE42148) and identified differentially expressed genes (DEGs) in samples from healthy subjects and patients with AMI. Also, we performed systematic gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used STRING to predict protein interactions. Moreover, MCC and MCODE algorithms in the cytoHubba plug-in were used to screen nine key genes in the network. We then determined the diagnostic significance of the nine obtained DEGs by plotting receiver operating characteristic curves using a multiscale curvature classification algorithm. Meanwhile, we investigated the relationship between AMI and immune checkpoints, ferroptosis, and m6A. In addition, we further validated the key genes through the GSE66360 dataset and consequently obtained nine specific genes that can be used as early diagnosis biomarkers for AMI. Through screening, we identified 210 DEGs, including 53 downregulated and 157 upregulated genes. According to GO, KEGG, and key gene screening results, FPR1, CXCR1, ELANE, TLR2, S100A12, TLR4, CXCL8, FPR2 and CAMP could be used for early prediction of AMI. Finally, we found that AMI was associated with ferroptosis, immune checkpoints, and m6A and FPR1, CXCR1, ELANE, TLR2, S100A12, TLR4, CXCL8, FPR2 and CAMP are effective markers for the diagnosis of AMI, which can provide new prospects for future studies on the pathogenesis of AMI.

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.

Results

A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.

Conclusions

A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04189-2.

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.

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

IMPACT STATEMENT

Nasopharyngeal cancer is one of the most common malignant tumors in the head and neck. Identification of promising miRNA biomarkers might benefit a lot to the detection of nasopharyngeal carcinoma. A three-miRNA signature (has-miR-142-3p, has-miR-29c, and has-miR-30e) was obviously associated with the overall survival of nasopharyngeal carcinoma patients. The model has better clinical independence and has better clinical prediction effect when combined with clinical characteristics. Our results revealed that a three-miRNA signature was a potential novel prognostic biomarker for nasopharyngeal carcinoma.

A Novel Diagnostic Nomogram for Noninvasive Evaluating Liver Fibrosis in Patients with Chronic Hepatitis B Virus Infection

Objective

To establish a novel nomogram for diagnosing liver fibrosis in patients with chronic hepatitis B virus (HBV) infection and verify the diagnostic performance of the established nomogram.

Methods

Patients with chronic HBV infection who met the inclusion and exclusion criteria were enrolled in this retrospective study; 70% and 30% of patients were randomly assigned to training dataset and validation dataset, respectively. The risk factors for liver fibrosis were screened using the univariate and multivariate logistic regression analyses. Based on the results, a nomogram was established and verified.

Results

508 patients with chronic HBV infection were included in this study (n = 355 for training dataset and n = 153 for validation dataset). The logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT) count, and prothrombin time (PT) were independent risk factors for liver fibrosis (P < 0.01), which were used to establish the nomogram. The consistency index (C-index) of the nomogram established for diagnosing liver fibrosis was 0.875. The calibration line and the ideal line were consistent, which indicated that diagnosis of liver fibrosis by the established model was accurate. The values of area under the receiver operator characteristic (ROC) curve (AUROC) for diagnosing liver fibrosis by the nomogram were 0.857 and 0.862 in the training dataset and validation dataset, respectively, which were noticeably higher than those in the well-known serological models, including the aspartate aminotransferase- (AST-) to-platelet ratio index (APRI) scoring model, fibrosis-4 (FIB-4) scoring model, APAG model (including age, PT, albumin, and γ-glutamyl transferase), and S-index model (all P < 0.05).

Conclusion

LSM, PT, and PLT were found as independent risk factors for liver fibrosis. The established nomogram exhibited an excellent diagnostic performance, and it can more visually and individually evaluate the probability of liver fibrosis in patients with chronic HBV infection.