Plasma Cytokines Pattern as a Prognostic Marker for Esophageal Squamous Cell Carcinoma via Unsupervised Clustering Analyses.

ABSTRACT:

Introduction

Cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL6), interferon-gamma (IFN-γ), interleukin 17-alpha (IL17-α), and interleukin 33 (IL33) play critical roles in immune responses and may impact cancer prognosis in future. However, few studies have simultaneously explored the prognostic impact of these cytokines for cancer. In this study, we aim to apply the unsupervised clustering analysis to approach the correlation between the expression of these cytokines and the subsequent prognosis of patients with esophageal squamous cell carcinoma (ESCC).

Methods

A robust clustering algorithm was used, the Gaussian mixture method (GMM), through the mclust R package to group patients based on the expression of their cytokines in plasma or tumors. The 32

SUBMITTER: Chuang CH 

PROVIDER: S-EPMC12048106 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature