Kavli Affiliate: Ke Wang
| First 5 Authors: Dietrich Kong, , , ,
| Summary:
MicroRNAs play an indispensable role in numerous biological processes ranging
from organismic development to tumor progression.In oncology,these microRNAs
constitute a fundamental regulation role in the pathology of cancer that
provides the basis for probing into the influences on clinical features through
transcriptome data. Previous work focused on machine learning (ML) for
searching biomarkers in different cancer databases, but the functions of these
biomarkers are fully not clear. Taking lung cancer as a prototype case of
study. Through integrating clinical information into the transcripts expression
data, we systematically analyzed the effect of microRNA on diagnostic and
prognostic factors at deteriorative lung adenocarcinoma (LUAD). After dimension
reduction, unsupervised hierarchical clustering was used to find the diagnostic
factors which represent the unique expression patterns of microRNA at various
patient’s stages. In addition, we developed a classification framework, Light
Gradient Boosting Machine (LightGBM) and SHAPley Additive explanation (SHAP)
algorithm, to screen out the prognostic factors. Enrichment analyses show that
the diagnostic and prognostic factors are not only enriched in cancer-related
athways, but also involved in many vital cellular signaling transduction and
immune responses. These key microRNAs also impact the survival risk of LUAD
patients at all (or a specific) stage(s) and some of them target some important
Transcription Factors (TF).The key finding is that five microRNAs
(hsa-mir-196b, hsa-mir-31, hsa-mir-891a, hsa-mir-34c, and hsa-mir-653) can then
serve as not only potential diagnostic factors but also prognostic tools in the
monitoring of lung cancer.
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