Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.

Backgrounds: Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet. Methods: The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs. Results: We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity. Conclusions: Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.

Uncovering hub genes and immunological characteristics for heart failure utilizing RRA, WGCNA and Machine learning.

Background: Heart failure (HF) is a major public health issue with high mortality and morbidity. This study aimed to find potential diagnostic markers for HF by the combination of bioinformatics analysis and machine learning, as well as analyze the role of immune infiltration in the pathological process of HF. Methods: The gene expression profiles of 124 HF patients and 135 nonfailing donors (NFDs) were obtained from six datasets in the NCBI Gene Expression Omnibus (GEO) public database. We applied robust rank aggregation (RRA) and weighted gene co-expression network analysis (WGCNA) method to identify critical genes in HF. To discover novel diagnostic markers in HF, three machine learning methods were employed, including best subset regression, regularization technique, and support vector machine-recursive feature elimination (SVM-RFE). Besides, immune infiltration was investigated in HF by single-sample gene set enrichment analysis (ssGSEA). Results: Combining RRA with WGCNA method, we recognized 39 critical genes associated with HF. Through integrating three machine learning methods, FCN3 and SMOC2 were determined as novel diagnostic markers in HF. Differences in immune infiltration signature were also found between HF patients and NFDs. Moreover, we explored the potential associations between two diagnostic markers and immune response in the pathogenesis of HF. Conclusions: In summary, FCN3 and SMOC2 can be used as diagnostic markers of HF, and immune infiltration plays an important role in the initiation and progression of HF.

Hemophagocytic lymphohistiocytosis and disseminated intravascular coagulation are underestimated, but fatal adverse events in chimeric antigen receptor T-cell therapy.

Hematotoxicity is the most common long-term adverse event (AE) after chimeric antigen receptor T-cell (CAR T) therapy. However, patients who receive CAR T therapy in pivotal clinical trials are subjected to restrictive selection criteria, and this means that rare but fatal toxicities are underestimated. Here, we systematically analyzed CAR T-associated hematologic AE using the US Food and Drug Administration Adverse Event Reporting System (FAERS) between January 2017 and December 2021. Disproportionality analyses were performed using reporting odds ratios (ROR) and information component (IC); the lower limit of the ROR and IC 95% confidence interval (CI) (ROR025 and IC025) exceeding one and zero was considered significant, respectively. Among the 105,087,611 reports in FAERS, 5,112 CAR T-related hematotoxicity reports were identified. We found 23 significant over-reporting hematologic AE (ROR025 >1) compared to the full database, of which hemophagocytic lymphohistiocytosis (HLH; n=136 [2.7%], ROR025 = 21.06), coagulopathy (n=128 [2.5%], ROR025 = 10.43), bone marrow failure (n=112 [2.2%], ROR025 = 4.88), disseminated intravascular coagulation (DIC; n=99 [1.9%], ROR025 = 9.64), and B-cell aplasia (n=98 [1.9%], ROR025 = 118.16, all IC025 > 0) were highly under-reported AE in clinical trials. Importantly, HLH and DIC led to mortality rates of 69.9% and 59.6%, respectively. Lastly, hematotoxicity-related mortality was 41.43%, and 22 death-related hematologic AE were identified using LASSO regression analysis. These findings could help clinicians in the early detection of those rarely reported but lethal hematologic AE, thus reducing the risk of severe toxicities for CAR T recipients.

Identification of m7G regulator-mediated RNA methylation modification patterns and related immune microenvironment regulation characteristics in heart failure.

BACKGROUND: N7-methylguanosine (m7G) modification has been reported to regulate RNA expression in multiple pathophysiological processes. However, little is known about its role and association with immune microenvironment in heart failure (HF). RESULTS: One hundred twenty-four HF patients and 135 nonfailing donors (NFDs) from six microarray datasets in the gene expression omnibus (GEO) database were included to evaluate the expression profiles of m7G regulators. Results revealed that 14 m7G regulators were differentially expressed in heart tissues from HF patients and NFDs. Furthermore, a five-gene m7G regulator diagnostic signature, NUDT16, NUDT4, CYFIP1, LARP1, and DCP2, which can easily distinguish HF patients and NFDs, was established by cross-combination of three machine learning methods, including best subset regression, regularization techniques, and random forest algorithm. The diagnostic value of five-gene m7G regulator signature was further validated in human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, consensus clustering algorithms were used to categorize HF patients into distinct molecular subtypes. We identified two distinct m7G subtypes of HF with unique m7G modification pattern, functional enrichment, and immune characteristics. Additionally, two gene subgroups based on m7G subtype-related genes were further discovered. Single-sample gene-set enrichment analysis (ssGSEA) was utilized to assess the alterations of immune microenvironment. Finally, utilizing protein-protein interaction network and weighted gene co-expression network analysis (WGCNA), we identified UQCRC1, NDUFB6, and NDUFA13 as m7G methylation-associated hub genes with significant clinical relevance to cardiac functions. CONCLUSIONS: Our study discovered for the first time that m7G RNA modification and immune microenvironment are closely correlated in HF development. A five-gene m7G regulator diagnostic signature for HF (NUDT16, NUDT4, CYFIP1, LARP1, and DCP2) and three m7G methylation-associated hub genes (UQCRC1, NDUFB6, and NDUFA13) were identified, providing new insights into the underlying mechanisms and effective treatments of HF.

Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis.

Background: Heart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays. Methods: Using four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages "clusterProfiler" and "GSVA" were utilized for enrichment analysis. Moreover, the transcription factor (TF)-DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset. Results: A total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF-DEG regulatory network was constructed, and 13 significant TF-DEG pairs were finally identified. Conclusion: Our study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

The Predictive Value of Myoglobin for COVID-19-Related Adverse Outcomes: A Systematic Review and Meta-Analysis.

Objective: Cardiac injury is detected in numerous patients with coronavirus disease 2019 (COVID-19) and has been demonstrated to be closely related to poor outcomes. However, an optimal cardiac biomarker for predicting COVID-19 prognosis has not been identified. Methods: The PubMed, Web of Science, and Embase databases were searched for published articles between December 1, 2019 and September 8, 2021. Eligible studies that examined the anomalies of different cardiac biomarkers in patients with COVID-19 were included. The prevalence and odds ratios (ORs) were extracted. Summary estimates and the corresponding 95% confidence intervals (95% CIs) were obtained through meta-analyses. Results: A total of 63 studies, with 64,319 patients with COVID-19, were enrolled in this meta-analysis. The prevalence of elevated cardiac troponin I (cTnI) and myoglobin (Mb) in the general population with COVID-19 was 22.9 (19-27%) and 13.5% (10.6-16.4%), respectively. However, the presence of elevated Mb was more common than elevated cTnI in patients with severe COVID-19 [37.7 (23.3-52.1%) vs.30.7% (24.7-37.1%)]. Moreover, compared with cTnI, the elevation of Mb also demonstrated tendency of higher correlation with case-severity rate (Mb, r = 13.9 vs. cTnI, r = 3.93) and case-fatality rate (Mb, r = 15.42 vs. cTnI, r = 3.04). Notably, elevated Mb level was also associated with higher odds of severe illness [Mb, OR = 13.75 (10.2-18.54) vs. cTnI, OR = 7.06 (3.94-12.65)] and mortality [Mb, OR = 13.49 (9.3-19.58) vs. cTnI, OR = 7.75 (4.4-13.66)] than cTnI. Conclusions: Patients with COVID-19 and elevated Mb levels are at significantly higher risk of severe disease and mortality. Elevation of Mb may serve as a marker for predicting COVID-19-related adverse outcomes. Prospero Registration Number: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020175133, CRD42020175133.