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lncRNA - Long Non-coding RNAs

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Related Experiment Video

Updated: Jun 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predicting lymphoma prognosis using machine learning-based genes associated with lactylation.

Miao Zhu¹, Qin Xiao², Xinzhen Cai³

¹Department of Hematology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou 225001, China; The Key Laboratory of Syndrome Differentiation and Treatment of Gastric Cancer of the State, Administration of Traditional Chinese Medicine, Yangzhou University, Yangzhou 225001, China; Yangzhou Hematology Laboratory, Yangzhou 225001, China.

Translational Oncology

|August 15, 2024

Summary

This summary is machine-generated.

Lactylation, a new modification, impacts lymphoma prognosis and drug response. A novel lactylation Riskscore model helps stratify patients and guide treatment selection for diffuse large B-cell lymphoma.

Keywords:

Diffuse large B-cell lymphoma HNRNPH1 Lactylation Machine learning Riskscore

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Oncology
Biochemistry
Molecular Biology

Background:

Lactylation, a post-translational modification (PTM) involving lactic acid, is implicated in solid tumor progression.
While high lactic acid levels are observed in lymphoma patients, the role of lactylation remains largely uninvestigated.
This study addresses the knowledge gap regarding lactylation's involvement in lymphoma.

Purpose of the Study:

To identify lactylation-related genes associated with lymphoma.
To evaluate the prognostic and predictive value of these genes in diffuse large B-cell lymphoma (DLBCL).
To develop a risk stratification model based on lactylation markers.

Main Methods:

Analysis of TCGA and GEO datasets for lactylation-related gene expression in DLBCL.
Development of a prognostic risk score model using COX regression.
Functional validation of key genes via cell and mouse models.
Examination of lactylation in clinical lymphoma specimens.

Main Results:

Identified 70 lactylation-related genes significantly associated with DLBCL prognosis.
Developed a lactylation Riskscore model that correlates with patient outcomes and immune infiltration.
High-risk patients exhibited chemoresistance but responded to immunotherapy.
HNRNPH1 was identified as a key regulator affecting prognosis, apoptosis, and cell cycle.

Conclusions:

Lactylation plays a significant role in DLBCL prognosis, immune microenvironment, and therapeutic response.
The developed Riskscore model facilitates patient stratification and personalized treatment strategies.
HNRNPH1 is a critical lactylation regulator influencing DLBCL patient outcomes.