Identification of diagnostic biomarkers for idiopathic pulmonary hypertension with metabolic syndrome by bioinformatics and machine learning

Lu, Wenzhang; Huang, Jinbo; Shen, Qin; Sun, Fei; Li, Jun

doi:10.1038/s41598-023-27435-4

Cited by 5 publications

(4 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SAC3D1, or SHD1, is implicated in centrosome duplication and mitotic progression, potentially mediating cell cycle regulation via centrosome amplification ( 45 ). SAC3D1 is involved in immune response, as well as its association with metabolism-related signaling pathways, positions it as a key player in T cell exhaustion and provides valuable insights for prognosis and immunotherapy effectiveness in various cancers ( 46 , 47 ). PSIP1, also known as LEDGF/p75, participates in various biological processes and plays a significant role in lens epithelium differentiation into fiber cell terminals ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

Single-cell RNA-seq reveals T cell exhaustion and immune response landscape in osteosarcoma

Fan,

Wang,

Cheng

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

BackgroundT cell exhaustion in the tumor microenvironment has been demonstrated as a substantial contributor to tumor immunosuppression and progression. However, the correlation between T cell exhaustion and osteosarcoma (OS) remains unclear.MethodsIn our present study, single-cell RNA-seq data for OS from the GEO database was analysed to identify CD8+ T cells and discern CD8+ T cell subsets objectively. Subgroup differentiation trajectory was then used to pinpoint genes altered in response to T cell exhaustion. Subsequently, six machine learning algorithms were applied to develop a prognostic model linked with T cell exhaustion. This model was subsequently validated in the TARGETs and Meta cohorts. Finally, we examined disparities in immune cell infiltration, immune checkpoints, immune-related pathways, and the efficacy of immunotherapy between high and low TEX score groups.ResultsThe findings unveiled differential exhaustion in CD8+ T cells within the OS microenvironment. Three genes related to T cell exhaustion (RAD23A, SAC3D1, PSIP1) were identified and employed to formulate a T cell exhaustion model. This model exhibited robust predictive capabilities for OS prognosis, with patients in the low TEX score group demonstrating a more favorable prognosis, increased immune cell infiltration, and heightened responsiveness to treatment compared to those in the high TEX score group.ConclusionIn summary, our research elucidates the role of T cell exhaustion in the immunotherapy and progression of OS, the prognostic model constructed based on T cell exhaustion-related genes holds promise as a potential method for prognostication in the management and treatment of OS patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Single-cell RNA-seq reveals T cell exhaustion and immune response landscape in osteosarcoma

Fan,

Wang,

Cheng

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…When it comes to cardiovascular diseases, AI also plays an important role in their diagnosis. From the acute diagnosis of left ventricular hypertrophy using imaging methods such as echocardiography [ 56 ], to the diagnosis of amyloidosis also based on TTE [ 66 ] or idiopathic pulmonary hypertension [ 157 ], artificial intelligence has demonstrated its power to help clinicians.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have focused on the effects of AI on pulmonary hypertension, from the prediction of this rare pathology in adults [ 147 , 150 , 155 , 161 , 162 ] or children [ 151 , 163 , 165 ] to the prediction of survival [ 154 , 167 ] or risk in patients with pulmonary hypertension [ 156 ], diagnosis of pulmonary hypertension [ 149 , 152 , 153 , 156 , 157 , 158 , 160 , 169 ], and the treatment of this disease [ 148 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

View full text Add to dashboard Cite

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

show abstract

“…To compare and order characteristics by relevance, we created random forest classi ers using the "random forest" R package [25]. Then, we merged the transcriptome data to perform a regression analysis using the LASSO Cox method, utilizing the "glmnet" R package [26], [27]. In order to get the best model, Additionally, we executed a vefold crossvalidation procedure.…”

Section: Functional Enrichment Analysismentioning

confidence: 99%

Discovering the diagnostic biomarkers underlying Type 1 diabetes and Celiac disease by integrating transcriptomics and machine learning

Selvam,

et al. 2024

Preprint

View full text Add to dashboard Cite

An immune-mediated disease with a long latency period is type 1 diabetes (T1D). The beta cells in the pancreatic islets die due to antibody-mediated mechanisms in T1D. The most common chronic disorders are celiac disease (CD). A specific serum antibody response characterizes CD, a complicated systemic immune-mediated enteropathy. Due to the immunological driven nature of both CD and T1D, the risk factors are comparable. Both are increasing at a global rate, T1D screening is advised since CD and autoimmune diseases like T1D frequently co-occur. Based on a large number of research, even if the exact pathophysiology is yet unknown. Our understanding of one disease affects the way we treat as we discover about another, and vice versa. The goal of this study is to look at the connections between both conditions to identify biomarkers that could potentially utilized to diagnose the two. We conducted a comprehensive analysis of the Differentially Expressed Genes (DEGs) identified in the samples using a range of bioinformatics techniques and machine learning algorithms, built a network for the biological interactions, and discovered 3 potential diagnostic genes with statistical values (NAA15, RPL21, and HCLS1) as legitimate candidate genes as the biomarker for the diagnosis of T1D and CD.

show abstract

Identification of diagnostic biomarkers for idiopathic pulmonary hypertension with metabolic syndrome by bioinformatics and machine learning

Cited by 5 publications

References 60 publications

Single-cell RNA-seq reveals T cell exhaustion and immune response landscape in osteosarcoma

Single-cell RNA-seq reveals T cell exhaustion and immune response landscape in osteosarcoma

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Discovering the diagnostic biomarkers underlying Type 1 diabetes and Celiac disease by integrating transcriptomics and machine learning

Contact Info

Product

Resources

About