Identification and Experimental Verification of PDK4 as a Potential Biomarker for Diagnosis and Treatment in Rheumatoid Arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by sustained joint inflammation, with an etiology that remains elusive. Achieving an early and precise diagnosis poses significant challenges. This study aims to elucidate the molecular pathways involved in RA pathogenesis by screening genes associated with its occurrence, analyzing the related molecular activities, and ultimately developing more effective molecular-level treatments for RA. METHODS: Microarray expression profiling datasets GSE1919, GSE10500, GSE15573, GSE77298, GSE206848, and GSE236924 were sourced from the Gene Expression Omnibus (GEO) database. Samples were divided into experimental (RA) and control (normal) groups. Differentially expressed genes (DEGs) were identified using R software packages such as limma, glmnet, e1071 as well as randomForest. Cross-validation of DEGs was conducted using lasso regression and the random forest (RF) algorithm in R software to pinpoint intersecting genes that met the criteria. Among these, one gene was selected as the target for correlation analysis to identify DEGs related to the target gene. Enrichment analysis utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases and Gene Ontology (GO) data. Gene Set Enrichment Analysis (GSEA) was performed to compare the expression levels of the target gene (PDK4) across various biological pathways and functions in groups with high and low expression. The relationship between target gene expression levels and cellular immune function was assessed using the immune function score technique. The discrepancy in immune cell distribution between the control and experimental groups, as well as their correlation with target gene expression levels, was elucidated using CIBERSORT. The relationships between mRNA, lncRNA, and miRNA were depicted in the ceRNA regulation network. The expression levels of the target gene were validated using Western blot and qRT-PCR. RESULTS: In this study, six intersecting genes meeting the criteria were identified through cross-validation, and PDK4 was chosen as the target gene for further investigation. Functional analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that PDK4-associated DEGs are primarily enriched in the PPAR signaling pathway, thereby regulating synovial cell proliferation and migration, ultimately influencing the onset and progression of rheumatoid arthritis (RA). Immune infiltration analysis suggested that eosinophil quantity may influence the progression of RA. Experimental results from PCR and Western blot confirmed the downregulation of PDK4 in the RA group. CONCLUSION: The significant downregulation of PDK4 expression in patients diagnosed with rheumatoid arthritis (RA) was confirmed. PDK4 may function as a novel regulatory factor in the onset and progression of RA, with potential applications as a diagnostic biomarker for the condition.

Outcomes of thoracic endovascular aortic repair with physician-manufactured partial micropore stent grafts for aortic arch pathologies.

OBJECTIVE: Thoracic endovascular aortic repair (TEVAR) has been used extensively in the management of thoracic aortic diseases. Numerous efforts have been made to enhance clinical outcomes through the use of stent grafts. This study aimed to investigate the effectiveness and safety of physician-manufactured partial micropore stent grafts (PSMGs) in TEVAR. METHODS: Between December 2017 and June 2022, data were collected from 56 patients who underwent TEVAR using physician-manufactured PSMGs. The evaluation encompassed technical success, perioperative and follow-up morbidity and mortality, stroke incidence, and branch artery patency. RESULTS: In this investigation, 56 patients received treatment with physician-manufactured PSMGs. Of these patients, 46 were male, with a mean age of 62.1 ± 11.2 years. Aortic pathologies comprised aortic dissection (n = 31 [55.4%]), aortic aneurysms (n = 10 [17.9%]), penetrating aortic ulcer (n = 8 [14.3%]), and intramural hematoma (n = 7 [12.5%]). During a median follow-up of 18 months (interquartile range, 13-25 months), the stroke rate, supra-aortic branch patency rate, and endoleak rate were 0%, 100%, and 7.1%, respectively. There were no occurrences of all-cause mortality, stroke, or the necessity for open conversion. CONCLUSIONS: TEVAR with physician-manufactured PSMGs is a viable alternative for addressing aortic arch pathologies in proficient medical centers. The approach demonstrates favorable branch patency, a low complication rate, and minimal postoperative mortality.

Machine learning identifies ferroptosis-related genes as potential diagnostic biomarkers for osteoarthritis.

Background: Osteoarthritis (OA) is one of the most common forms of degenerative arthritis and a major cause of pain and disability. Ferroptosis, a novel mode of cell death, has been verified to participate in the development of OA, but its mechanism is still unclear. This paper analyzed the ferroptosis-related genes (FRGs) in OA and explored their potential clinical value. Methods: We downloaded data through the GEO database and screened for DEGs. Subsequently, FRGs were obtained using two machine learning methods, LASSO regression and SVM-RFE. The accuracy of the FRGs as disease diagnosis was identified using ROC curves and externally validated. The CIBERSORT analyzed the immune microenvironment rug regulatory network constructed through the DGIdb. The competitive endogenous RNA (ceRNA) visualization network was constructed to search for possible therapeutic targets. The expression levels of FRGs were verified by qRT-PCR and immunohistochemistry. Results: In this study, we found 4 FRGs. The ROC curve showed that the combined 4 FRGs had the highest diagnostic value. Functional enrichment analysis showed that the 4 FRGs in OA could influence the development of OA through biological oxidative stress, immune response, and other processes. qRT-PCR and immunohistochemistry verified the expression of these key genes, further confirming our findings. Monocytes and macrophages are heavily infiltrated in OA tissues, and the persistent state of immune activation may promote the progression of OA. ETHINYL ESTRADIOL was a possible targeted therapeutic agent for OA. Meanwhile, ceRNA network analysis identified some lncRNAs that could regulate the FRGs. Conclusion: We identify 4 FRGs (AQP8, BRD7, IFNA4, and ARHGEF26-AS1) closely associated with bio-oxidative stress and immune response, which may become early diagnostic and therapeutic targets for OA.

Autophagy-related long non-coding RNA prognostic model predicts prognosis and survival of melanoma patients.

BACKGROUND: Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. Long non-coding RNAs (lncRNAs) are key factors in the occurrence and development of many malignant tumors, and are involved in the prognosis of some patients. AIM: To identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis, treatment, and prognosis of melanoma patients. METHODS: We retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas (TCGA) database. Then, we identified autophagy-related genes in the Human Autophagy Database. Using R, coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels. We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma. Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established. The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores. Based on the risk scores, melanoma patients were randomly divided into high- and low-risk groups. Receiver operating characteristic curve analysis, dependent on time, was performed to assess the accuracy of the prognostic model. At the same time, we also downloaded the melanoma data sets GSE65904, GSE19234, and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification. Finally, we performed Gene Set Enrichment Analysis functional annotation, which showed that the low and the high-risk groups had different enriched pathways. RESULTS: The co-expression network for autophagy-related genes was constructed using R, and 936 lncRNAs related to autophagy were identified. Then, 52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients' survival by univariate Cox proportional risk analysis (P < 0.01). Further, the 52 autophagy-related lncRNAs mentioned above were analyzed by multivariate Cox analysis with R. Fifteen lncRNAs were selected: LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, PCED1B-AS1, HLA-DQB1-AS1, AC011374.2, LINC00324, AC018553.1, LINC00520, DBH-AS1, and ITGB2-AS1. The P values in all survival analyses using these 15 lncRNAs were < 0.05. These lncRNAs were used to build a risk model based on the risk score. Negative correlations were observed between risk scores and overall survival rate in melanoma patients over time. Additionally, the melanoma risk curve and scatter plot analyses showed that the death number increased along with the increase in the risk score. Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs. The risk model constructed with these lncRNAs can help and guide melanoma patient prognosis predictions and individualized treatments in the future. CONCLUSION: Overall, the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.

Correlative Study on the Relationship between the Expression of m6a-Related Genes and the Prognosis and Immunotherapy of Soft Tissue Sarcoma.

Background: Soft tissue sarcomas (STS) are rare malignancies arising from mesenchymal tissue and interlacing ectodermal nerve tissue. Immunotherapy plays an important role in the prognosis and survival of STS patients. However, there is insufficient evidence to confirm the prognostic value of m6A-related genes and to evaluate the efficacy of immunotherapy for STS. Methods: We analyzed 23 m6A regulators from STS samples using R software and defined the modification patterns for three STS m6A regulators. Then, we constructed the m6A scores and divided the samples into high and low subgroups. Finally, we used data from the GEO database to verify the results. Results: We found that the m6A clusters differed in the overall survival (OS), progression-free survival (PFS), and immune infiltration rate. Additionally, the m6A score was positively correlated with the contents of activated B cells, activated dendritic cells, CD56 bright natural killer cells, helper T cells, and regulatory T cells. The group with a higher m6A score also presented higher OS and PFS rates. Regarding immunotherapy, STS patients with a high m6A score presented better results. Consistently, we found similar results in another dataset with patients that received anti-PD-1/PD-L1 therapy. Conclusion: Our current results indicated that the m6A score can be used to assess the survival rate of STS patients and guide immunotherapy and predict its effects. The analysis of different m6A patterns of STS samples contributed to the understanding of the diversity and complexity of the tumor microenvironment (TME) and provided new ideas for the clinical development of personalized immunotherapy and prediction of the prognosis of STS patients.

Pyroptosis-related gene mediated modification patterns and immune cell infiltration landscapes in cutaneous melanoma to aid immunotherapy.

Tumor occurrence, infiltration, and metastasis are significantly affected by the tumor microenvironment (TME). Increasing evidence has elucidated TME's clinical significance in prognostic assessment and immunotherapy efficacy. Nonetheless, no studies have reported the potential pyroptosis-related genes (PRGs) function in TME immune cell infiltration. In this study, we systematically analyzed different PRG modification patterns in 685 cutaneous melanoma (CM) cases. We comprehensively explored the relationship between these PRG modification patterns and TME cell infiltration characteristics. Then, we used principal component analysis to construct a pyroptosis scoring system to quantify the PRG modification patterns in each CM patient. Three different PRG modification patterns were identified. Pyroptosis score was confirmed as an independent prognostic factor for CM patients. High pyroptosis score was characterized by high immunophenscore and more lymphocytes infiltration, such as T, B, and NK cells - indicating a strong ability to monitor and clear tumors, which may be responsible for the advantageous survival. Three independent cohorts that received immunotherapy confirmed the significant therapeutic efficacy and clinical benefit in high pyroptosis scores patients. This study revealed that the PRG modification patterns have a crucial effect on the CM complex and diverse microenvironment. Pyroptosis scores might serve as credible predictors of immunotherapy response and prognostic assessment. This provides a new direction for personalized immunotherapy strategies and appropriate immunotherapy candidates screening.