Precise Molecular Subtyping Reveals Heterogeneity of Lung Adenocarcinoma Based on DNA Methylation
Authors: Shi Jiaxin , Zhang Mengyan , Su Mu , Peng Bo , Xu Ran , Wang Chenghao , Zhou Xiang , Zhang Yan , Zhang Linyou 
Published: June 2024

Background:

Due to the high heterogeneity of lung adenocarcinoma (LUAD),
which restricts the effectiveness of therapy, precise molecular subgrouping of LUAD
is of great significance. Clinical research has demonstrated the significant potential
of DNA methylation as a classification indicator for human malignancies.

Methods:

WGML framework (which was developed based on weighted gene correlation
network analysis (WGCNA), Gene Ontology (GO), and machine learning) was developed
to precisely subgroup molecular subtypes of LUAD. This framework included
two parts: the WG algorithm and the machine learning part. The WG algorithm part was
an original algorithm used to obtain a crucial module, which was characterized by
weighted correlation network analysis, functional annotation, and mathematical algorithms.
The machine learning part utilized the Boruta algorithm, random forest algorithm,
and Gradient Boosting Regression Tree algorithm to select feature genes. Then,
based on the results of the WGML framework, subtypes were computed by the hierarchical
clustering algorithm. A series of analyses, including dimensionality reduction methods,
survival analysis, clinical stage analysis, immune infiltration analysis, tumor environment
analysis, immune checkpoints analysis, TIDE analysis, CYT analysis, somatic
mutation analysis, and drug sensitivity analysis, were utilized to demonstrate the effectiveness
of subgrouping. GEO datasets were used to externally validate the results.
Meanwhile, another subgrouping method of LUAD from another study was employed to
compare with the WGML framework.

Result:

By importing DNA methylation data into the WGML framework, nine genes
were obtained to further subgroup LUAD. Three subtypes, the Carcinogenesis subtype,
Immune-infiltration subtype, and Chemoresistance subtype, were identified. The dimensionality
reduction method exhibited great distinctness between subtypes. A series of
analyses were employed to exhibit the difference among the three subtypes and to demonstrate
the accuracy of the definition of subtypes. Besides, the WGML framework
was compared with a LUAD subgrouping method from another research, which demonstrated
that WGML had better efficiency for subgrouping LUAD.

Conclusion:

This study provides a novel LUAD subgrouping framework named
WGML for the accurate subgrouping of lung adenocarcinoma.

result:

By importing DNA methylation data into WGML framework, nine genes were obtained to further subgroup LUAD. Three subtypes, Carcinogenesis subtype, Immune-infiltration subtype, and Chemoresistance subtype were identified. Dimensionality reduction method exhibited great distinctness between subtypes. A series analyses were employed to exhibit the difference of three subtypes and to demonstrate accuracy of definition of subtypes. Besides, WGML framework was compared with a LUAD subgrouping method from another research, which demonstrated WGML had better efficiency for subgrouping LUAD.