A prognostic nomogram integrating novel biomarkers identified by machine learning for cervical squamous cell carcinoma.

ABSTRACT:

Background

Cervical cancer (CC) represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurately identifying high-risk patients. Here, we provided a combination machine learning algorithm-based signature to predict the prognosis of cervical squamous cell carcinoma (CSCC).

Methods and materials

After utilizing RNA sequencing (RNA-seq) data from 36 formalin-fixed and paraffin-embedded (FFPE) samples, the most significant modules were highlighted by the weighted gene co-expression network analysis (WGCNA). A candidate genes-based prognostic classifier was constructed by the least absolute shrinkage and selection operator (LASSO) and then validated in an independent validation

SUBMITTER: Li Y 

PROVIDER: S-EPMC7275455 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature