machine-learning-model-for-predicting-risk-factor-analysis-mortality

Machine Learning Model for Predicting Risk Factor Analysis and a Mortality Prediction Model of Acute Cholangitis Complicated with Sepsis

This study examined factors associated with sepsis in acute cholangitis and developed outcome-specific models for sepsis and mortality. We retrospectively ...

Binhai County People's Hospital

Immune checkpoint blockade (ICB) therapy has transformed the treatment of triple-negative breast cancer (TNBC) in recent years. However, some TNBC patients with high programmed death-ligand 1 (PD-L1) expression levels develop immune checkpoint resistance. Hence, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to construct prognostic models of patient survival outcomes in order to understand biological mechanisms operating within the tumor microenvironment.

Identification of immunosuppressive signature subtypes and prognostic risk signatures in triple-negative breast cancer.

The advent of immune checkpoint blockade (ICB) therapies this year has changed the way glioblastoma (GBM) is treated. Meanwhile, some patients with strong PD-L1 expression remain immune checkpoint resistant. To better understand the molecular processes that influence the immune environment, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to predict patient survival outcomes.

Identification of cytokine-predominant immunosuppressive class and prognostic risk signatures in glioma.

We performed UCC on the expression data of lung squamous cell carcinoma tumor samples to identify the classification of lung squamous cell carcinoma (LUSC) tumor samples, and calculated the levels of different classified immune cells by single-sample gene enrichment analysis (ssGSEA) to obtain a set of immune-related subtype gene tags, which can be used for subtype classification of lung squamous cell carcinoma. TCGA-LUSC and GSE30219 data of lung squamous cell carcinoma were obtained from TCGA and GEO databases. Prognostic-associated subtypes were identified by unsupervised consensus clustering (UCC). Using ssGSEA analysis to calculate the level of immune cells of different subtypes, obtain the connection between subtypes and immunity, identify the gene signatures recognized by subtypes, and verify this group of gene signatures through GSE30219. We effectively identified 2 subtypes that were significantly associated with prognostic survival by UCC, and calculated according to ssGSEA, the 2 subtypes were significantly different at the level of immune cells, followed by introducing a This weighted thinking computes a set of gene signatures that are significantly associated with subtype 1. During validation, this set of gene signatures could efficiently and robustly identify distinct prognostic immune subtypes, demonstrated the validity of this set of gene signatures, as well as 2 subtypes of lung squamous cell carcinoma. We used lung squamous cell carcinoma data from public databases and identified 2 prognostic immunosubtypes of lung squamous cell carcinoma and a set of gene tags that can be used to classify immune subtypes of lung squamous cell carcinoma, which may provide effective evidence for accurate clinical treatment of lung squamous cell carcinoma.

Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering.

The role of adipokines in the development of lung diseases is significant, yet their specific relationship with different lung diseases remains unclear.

Article	Year
Identification of immunosuppressive signature subtypes and prognostic risk signatures in triple-negative breast cancer. Frontiers in oncology\| PubMed ID: 37377907	2023
Identification of cytokine-predominant immunosuppressive class and prognostic risk signatures in glioma. Journal of cancer research and clinical oncology\| PubMed ID: 37479756	2023
Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering. Medicine\| PubMed ID: 37713826	2023
The association between adipokines and pulmonary diseases: a mendelian randomization study. BMC pulmonary medicine\| PubMed ID: 38263093	2024

Yuhan Wang

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