Genome-Wide Identification of Immune-Related Alternative Splicing and Splicing Regulators Involved in Abdominal Aortic Aneurysm

Predictive model for novel subtypes of patients undergoing lower extremity amputation for peripheral artery disease: An unsupervised machine learning study

Background

Peripheral artery disease (PAD) represents the frequently seen circulatory condition related to a risk of critical limb ischemia and amputation. Critical lower extremity ischemia may require amputation, and the outcomes vary. In this study, we developed an artificial intelligence (AI)-driven predictive model for PAD subtypes to assess risk among patients more precisely and accurately to predict disease progression.

Methods

The present retrospective study examined clinical data in PAD patents undergoing lower extremity amputation. The data were analyzed using an unsupervised machine learning algorithm (UMLA) for subgroup identification and risk stratification. The clustering result accuracy was validated by analyzing the follow-up data of clusters. Finally, we built the prediction model with binary logistic regression.

Results

In total, we enrolled 507 cases into this work. Two distinct subgroups, consisting of Clusters 1 and 2, were identified by UMLA; those from Cluster 1 showed markedly poorer conditions and prognostic outcomes compared with those from Cluster 2. With regard to the new PAD subtype, we established a nomogram with eight predictive factors, including gender, age, smoking history, diabetes and coronary heart disease history, albumin levels, endovascular intervention, and amputation level. The nomogram could accurately categorize patients into two identified clusters, and the area under receiver operating characteristic curve was 0.861 (95 % confidence interval: 0.830-0.893).

Conclusion

In this study, UMLA was used to identify new phenotypic subgroups among PAD cases who showed different risks of amputation. Our constructed AI-driven predictive model for PAD subtypes showed that it can be used for risk stratification and clinical management with high accuracy and reliability.

Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity

A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner.

The plant protection preparation GZM improves crop immunity, yield, and quality

Lauryl alcohol, a natural compound found in plants and other organisms, is widely used to make surfactants, food, and pharmaceuticals. GZM, a plant protection preparation with lauryl alcohol as its major component is thought to establish a physical barrier on the plant surface, but its physiological functions are unknown. Here, we show that GZM improves the performance of peanut (Arachis hypogaea) plants in both the laboratory and the field. We demonstrate that the treatment with GZM or lauryl alcohol raises the contents of several specific lysophospholipids and induces the biosynthesis of phenylpropanoids, flavonoids, and wax in various plant species. In the field, GZM improves crop immunity, yield, and quality. In addition, GZM and lauryl alcohol can inhibit the growth of some pathogenic fungi. Our findings provide insights into the physiological and biological effects of GZM treatment on plants and show that GZM and lauryl alcohol are promising preparations in agricultural production.

The mechanism and therapy of aortic aneurysms

Aortic aneurysm is a chronic aortic disease affected by many factors. Although it is generally asymptomatic, it poses a significant threat to human life due to a high risk of rupture. Because of its strong concealment, it is difficult to diagnose the disease in the early stage. At present, there are no effective drugs for the treatment of aneurysms. Surgical intervention and endovascular treatment are the only therapies. Although current studies have discovered that inflammatory responses as well as the production and activation of various proteases promote aortic aneurysm, the specific mechanisms remain unclear. Researchers are further exploring the pathogenesis of aneurysms to find new targets for diagnosis and treatment. To better understand aortic aneurysm, this review elaborates on the discovery history of aortic aneurysm, main classification and clinical manifestations, related molecular mechanisms, clinical cohort studies and animal models, with the ultimate goal of providing insights into the treatment of this devastating disease. The underlying problem with aneurysm disease is weakening of the aortic wall, leading to progressive dilation. If not treated in time, the aortic aneurysm eventually ruptures. An aortic aneurysm is a local enlargement of an artery caused by a weakening of the aortic wall. The disease is usually asymptomatic but leads to high mortality due to the risk of artery rupture.

Single-cell transcriptome in silico analysis reveals conserved regulatory programs in macrophages/monocytes of abdominal aortic aneurysm from multiple mouse models and human

Abdominal aortic aneurysm (AAA) is a life-threatening disease and there is currently a lack of effective treatment to prevent it rupturing. ScRNA-seq studies of AAA are still lacking. In the study, we analyzed the published AAA scRNA-seq datasets from the mouse elastase-induced model, CaCl₂ treatment model, Ang II-induced model and human by using bioinformatic approaches and in silico analysis. A total of 26 cell clusters were obtained and 11 cell types were identified from multiple mouse AAA models. Also, the proportion of Mφ/Mo increased in the AAA group and Mφ/Mo was divided into seven subtypes. There were significant differences in transcriptional regulation patterns of Mφ/Mo in different AAA models. The enrichment pathways of upregulated or downregulated genes from Mφ/Mo in the three mouse datasets were different. The actived regulons of Mφ/Mo had strong specificity and the repressed regulons showed high consistency. The co-upregulated genes as well as actived regulons and co-downregulated genes as well as repressed regulons were closely correlated and formed regulatory networks. Mφ/Mo from human AAA dataset was divided into five subtypes. The proportion of three macrophage subpopulations increased but the proportion of two monocyte subpopulations decreased. In the AAA group, the upregulated or downregulated genes of Mφ/Mo were enriched in different pathways. After further analyzing the genes in Mφ/Mo of both mouse and human scRNA-seq datasets, two genes were upregulated in the four datasets, IL-1B and THBS1. In conclusion, in silico analysis of scRNA-seq revealed that Mφ/Mo and their regulatory related genes as well as interaction networks played an important role in the pathogenesis of AAA.

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning

OBJECTIVE

The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared their clinical results.

METHODS

A total of 422 patients with spinal tuberculosis who received surgical treatment were enrolled. Clustering analysis was performed using the K-means clustering algorithm and the routinely available clinical data collected from patients within 24 h after admission. Finally, the differences in clinical characteristics, surgical efficacy, and postoperative complications among the subphenotypes were compared.

RESULTS

Two subphenotypes of spinal tuberculosis were identified. Laboratory examination results revealed that the levels of more than one inflammatory index in cluster 2 were higher than those in cluster 1. In terms of disease severity, Cluster 2 showed a higher Oswestry Disability Index (ODI), a higher visual analysis scale (VAS) score, and a lower Japanese Orthopedic Association (JOA) score. In addition, in terms of postoperative outcomes, cluster 2 patients were more prone to complications, especially wound infections, and had a longer hospital stay.

CONCLUSION

K-means clustering analysis based on conventional available clinical data can rapidly identify two subtypes of spinal tuberculosis with different clinical results. We believe this finding will help clinicians to rapidly and easily identify the subtypes of spinal tuberculosis at the bedside and become the cornerstone of individualized treatment strategies.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis

Backgrounds

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.

Methods

Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.

Results

A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.

Conclusions

We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

A novel inflammatory response-related signature predicts the prognosis of cutaneous melanoma and the effect of antitumor drugs

Cutaneous melanoma (CM) is a skin cancer that is highly metastatic and aggressive, with a dismal prognosis. This is the first study to use inflammatory response-related genes to build a model and evaluate their predictive significance in CM. This study used public databases to download CM patients' mRNA expression profiles and clinical data to create multigene prognostic markers in the UCSC cohort. We compared overall survival (OS) between high- and low-risk groups using the Kaplan-Meier curve and determined independent predictors using Cox analysis. We also used enrichment analysis to assess immune cell infiltration fraction and immune pathway-related activity using KEGG enrichment analysis. Furthermore, we detected prognostic genes' mRNA and protein expression in CM and normal skin tissues using qRT-PCR and immunohistochemistry. Finally, we developed a 5-gene predictive model that showed that patients in the high-risk group had a considerably shorter OS than those in the low-risk group. The analysis of the receiver operating characteristic (ROC) curve proved the model's predictive ability. We also conducted a drug sensitivity analysis and discovered that the expression levels of prognostic genes were substantially linked with cancer cell sensitivity to antitumor medicines. The findings show that the model we developed, which consists of five inflammatory response-related genes, can be used to forecast the prognosis and immunological state of CM, giving personalized and precision medicine a new goal and direction.

Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics

Background

Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification.

Methods

A total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering.

Results

We identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different.

Conclusions

Our results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.

RNA-Binding Proteins and Alternative Splicing Genes Are Coregulated in Human Retinal Endothelial Cells Treated with High Glucose

To explore the relevant RNA-binding proteins (RBPs) and alternative splicing events (ASEs) in diabetic retinopathy (DR). We devised a comprehensive work to integrate analyses of the differentially expressed genes, including differential RBPs, and variable splicing characteristics related to DR in human retinal endothelial cells induced by low glucose and high glucose in dataset GSE117238. A total of 2320 differentially expressed genes (DEGs) were identified, including 1228 upregulated genes and 1092 downregulated genes. Further analysis screened out 232 RBP genes, and 42 AS genes overlapped DEGs. We selected high expression and consistency six RBP genes (FUS, HNRNPA2B1, CANX, EIF1, CALR, and POLR2A) for coexpression analysis. Through analysis, we found eight RASGs (MDM2, GOLGA2P7, NFE2L1, KDM4A, FAM111A, CIRBP, IDH1, and MCM7) that could be regulated by RBP. The coexpression network was conducted to further elucidate the regulatory and interaction relationship between RBPs and AS. Apoptotic progress, protein phosphorylation, and NF-kappaB cascade revealed by the functional enrichment analysis of RASGs regulated by RBPs were closely related to diabetic retinopathy. Furthermore, the expression of differentially expressed RBPs was validated by qRT-PCR in mouse retinal microvascular endothelial cells and retinas from the streptozotocin mouse model. The results showed that Fus, Hnrnpa2b1, Canx, Calr, and Polr2a were remarkedly difference in high-glucose-treated retinal microvascular endothelial cells and Fus, Hnrnpa2b1, Canx, and Calr were remarkedly difference in retinas from streptozotocin-induced diabetic mice compared to control. The regulatory network between identified RBPs and RASGs suggests the presence of several signaling pathways possibly involved in the pathogenesis of DR. The verified RBPs should be further addressed by future studies investigating associations between RBPs and the downstream of AS, as they could serve as potential biomarkers and targets for DR.