KLRB Receptor Family and Human Early Activation Antigen (CD69)

Gupta, Rajesh Kumar; Gupta, Garima

doi:10.1007/978-3-7091-1065-2_29

Cited by 1 publication

(1 citation statement)

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“…The first identified predicted gene is KLRB1 , identified by the LASSO. The receptor of KLRB1 has been reported to interact with and be correlated with CD69 [ 47 ], a well-known immune cell biomarker for cultured peripheral T cells [ 48 ]. During the activation of T cells, the expression of CD69 is upregulated [ 49 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods

Yang,

Zhang,

Ren

et al. 2023

Life

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Immune cell infiltration that occurs at the site of colon tumors influences the course of cancer. Different immune cell compositions in the microenvironment lead to different immune responses and different therapeutic effects. This study analyzed single-cell RNA sequencing data in a normal colon with the aim of screening genetic markers of 25 candidate immune cell types and revealing quantitative differences between them. The dataset contains 25 classes of immune cells, 41,650 cells in total, and each cell is expressed by 22,164 genes at the expression level. They were fed into a machine learning-based stream. The five feature ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, minimum redundancy maximum relevance, and random forest) were first used to analyze the importance of gene features, yielding five feature lists. Then, incremental feature selection and two classification algorithms (decision tree and random forest) were combined to filter the most important genetic markers from each list. For different immune cell subtypes, their marker genes, such as KLRB1 in CD4 T cells, RPL30 in B cell IGA plasma cells, and JCHAIN in IgG producing B cells, were identified. They were confirmed to be differentially expressed in different immune cells and involved in immune processes. In addition, quantitative rules were summarized by using the decision tree algorithm to distinguish candidate immune cell types. These results provide a reference for exploring the cell composition of the colon cancer microenvironment and for clinical immunotherapy.

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