TY  - EJOU
AU  - ZHANG, HENG 
AU  - CHENG, WENJING 
AU  - ZHAO, HAIBO 
AU  - CHEN, WEIDONG 
AU  - ZHANG, QIUJIE 
AU  - YU, QING-QING 

TI  - Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches
T2  - Oncology Research

PY  - 2024
VL  - 32
IS  - 2
SN  - 1555-3906

AB  -  <b>Background:</b> Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. <b>Methods:</b> The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox proportional regression analyses; then, depending upon these identified signature genes and clinical variables, a nomogram was generated. Lastly, to assess tumor immune characteristics, concomitant evaluation of tumor immune evasion/risk scoring were elucidated. <b>Results:</b> A 8-gene signature, including <i>ACBD4, ACOX1, CD36, CPT2, ELOVL3, ELOVL6, ENO3</i>, and <i>SUCLG2</i>, was generated, and depending upon this, CRC patients were categorized within high-risk (H-R) and low-risk (L-R) cohorts. Furthermore, risk and age-based nomograms indicated moderate discrimination and good calibration. The data confirmed that the 8-gene model efficiently predicted CRC patients’ prognosis. Moreover, according to the conjoint analysis of tumor immune evasion and the risk scorings, the H-R cohort had an immunosuppressive tumor microenvironment, which caused a substandard prognosis. <b>Conclusion:</b> This investigation established a FAMGs-based prognostic model with substantially high predictive value, providing the possibility for improved individualized treatment for CRC individuals.
KW  - Fatty acid metabolism; Colorectal cancer; Gene signatures; Machine learning

DO  - 10.32604/or.2023.043138